2.2. Asset Description

This section describes how a large-scale building inventory was constructed in two phases. The initial phase of this work involved identifying the attributes needed. The second phase comprised obtaining these attributes for each building using machine learning and computer vision techniques to create a core set of attributes. The remaining attributes were then obtained using other data sources as discussed below.

2.2.1. Phase I: Attribute Definition

All the attributes required for loss estimation were first identified to develop the Building Inventory data model. This Building Inventory data model presented in Table 2.2.1.1 provides a set of attributes that are assigned to each asset to form the building inventory file serving as input to the workflow. For each attribute a row in the table is provided. Each row has a number of columns: the attribute name, description, format (such as alphanumeric, floating-point number), the data source used to define that attribute. An expanded version of Table 2.2.1.1 with the full details of this data model is available on DesignSafe PRJ-3314.

Table 2.2.1.1 Building Inventory data model for Atlantic County Inventory.

Attribute

Description

Format

BldgID

Building unique ID. The first four characters will be “NJBF,” followed by a 9-digit zero-padded number.

NJBF#########

Address

Typically assessor field for property location. This is distinct from Street Address in MODIV which is associated with the billing address

Alphanumeric

City

Typically assessor field for property location city.

Alphanumeric

State

Typically assessor field for property location - state abbreviation.

Alphanumeric

Latitude

Latitude of the Building Centroid (inside polygon).

Floating point number (Decimal Degrees)

Longitude

Longitude of the Building Centroid (inside polygon).

Floating point number (Decimal Degrees)

OccupancyClass

Subclassifications of buildings across various categories of Residential (RES), Commercial (COM), Industrial (IND), Agriculture (AGR), Government (GOV), Education (EDU), Religious/Non-Profit (REL).

Choices: RES1, RES2, RES3A, RES3B, RES3C, RES3D, RES3E, RES3F, RES4, RES5, RES6, COM1, COM2, COM3, COM4, COM5, COM6, COM7, COM8, COM9, COM10, IND1, IND2, IND3, IND4, IND5, IND6, AGR1, REL1, GOV1, GOV2, EDU1, EDU2

BuildingType

Core construction material type; Wood, Concrete, Steel, Masonry, Manufactured Housing.

Choices: 3001, 3002, 3003, 3004, 3005

UseCode

Class 4 Use Codes (Field 67) applicable to commercial buildings only, describing specific use of commercial properties.

Integer (3-digit)

BldgClass

Building class according to NJ Appraisal Manual (see Vol 2)

Integer (3-digit)

EssentialClass

Designates several classes of essential facilities in the region based on open data to ensure they are properly modeled.

Choices: PS, EOC, HO, HS, SCH

DesignLevel

Classification of level of engineering used in design process: Choices: Engineered (E), Pre-Engineered, (PE) Marginally Engineered (ME), Non-Engineered (NE)

Choices: E, PE, ME, NE

YearBuiltNJDEP

Assessor-provided Year of Construction from NJDEP Footprints

Integer (4-digit)

YearBuiltMODIV

Assessor-provided Year of Construction from MODIV

Integer (4-digit)

NumberofStories0

Assessor-provided number of stories

Integer

NumberofStories1

Number of stories estimated via image processing

Integer

NoUnits

Number of units in the property (commercial or residential)

Integer

PlanArea0

Plan area in square feet from assessor databases

Floating point number

PlanArea1

Plan area in square feet from footprint data

Floating point Number

FoundationType

Classification using 7 types referenced by flood model

Integer (4-digit)

SplitLevel

Specifies if residential construction is split-level

Choices: Yes, No

ElevationR0

Elevation of the bottom plane of the roof (lowest edge of roof line) relative to grade (ft)

Floating point number

ElevationR1

Elevation of highest point of the roof (peak of gable or apex of hip) relative to grade (ft)

Floating point Number

FirstFloorHt0

Height above grade estimated from elevation certificate or inferred from foundation type (in feet): Defined as top of lowest/bottom floor

Floating point Number

FirstFloorHt1

Height of top of floor as estimated from base of door height above grade, based on streetview imagery (in feet)

Floating point Number

FloodZone

Flood zone specified on FEMA FIRM based on building location

Choices: 6101, 6102, 6103, 6104, 6105, 6106, 6107, 6108, 6109, 6110, 6111, 6112, 6113, 6114, 6115, 6199

DWSI

DesignWindSpeed I in mph

Floating point number

DWSII

DesignWindSpeed II in mph

Floating point number

DWSIII

DesignWindSpeed III in mph

Floating point number

DWSIV

DesignWindSpeed IV in mph

Floating point number

WindZone

HAZUS-defined Wind Zone (For Manuactured homes, based on HUD Designation)

Choices: I, II, III

AvgJanTemp

Average temperature in January below or above critial value of 25F.

Choices: Above, Below

RoofShape

Roof classified into equivalent hip, gable or flat

Choices: Hip, Gable, Flat

RoofSlope

Slope of roof covering the majority of the dwelling

Floating point number

RoofCover

Roof covering, specified only for residential construction.

Choices: 5701, 5702, 5703, 5704

MeanRoofHt

Mean height of roof system in ft

Floating point number

WindowArea

Percentage of walls defined by window openings

Floating point number (decimal<1)

Garage

Assessor-provided type of garage.

Floating point number

TerrainRoughness

HAZUS-defined terrain classifications (x100) based on LULC data

Choices: 3, 15, 35, 70, 100

AnalysisDefault

Defines the default level of fidelity for analysis

Choices: 1, 2, 3

AnalysisAdopted

Defines the adopted level of fidelity for analysis

Choices: 1, 2, 3

Modifications

Record of manual updates, corrections or modifications to record

Alphanumeric (freeform)

HazusClass-W

Hazus building classes as defined for wind hazards

CHOICES: WSF1, WSF2, WMUH1, WMUH2, WMUH3, WMUH1NE, WMUH2NE, WMUH3NE, WMUH4NE, MSF1, MSF2, MMUH1, MMUH2, MMUH3, MLRM1, MLRM2, MLRI, MERBL, MERBM, MERBH, MECBL, MECBM, MECBH, MMUH1NE, MMUH2NE, MMUH3NE, CERBL, CERBM, CERBH, CECBL, CECBM, CECBH, SPMBS, SPMBM, SPMBL, SERBL, SERBM, SERBH, SECBL, SECBM, SECBH, MHPHUD, MH76HUD, MH94HUD-I, MH94HUD-II, MH94HUD-III, HUEFFS, HUEFSS, HUEFSM, HUEFSL, HUEFHS, HUEFHM, HUEFHL, HUEFPS, HUEFEO

RoofSystem

Underlying roof structure, applies only to masonry buildings

Choices: Wood, OWSJ

HazardProneRegion

Defines Hazard Prone Regions (HPR) for the purposes of Hazus wind vulnerability assignments for WSF1-2

Choices: yes, no

WindBorneDebris

Defines Wind Borne Debris (WBD) for the purposes of Hazus wind vulnerability assignments for WSF1-2

Choices: yes, no

SecondaryWaterResistance

Defines Secondary Water Resistance (SWR) for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MSF1-2, MMUH1-3

Choices: yes, no

RoofCover

Defines roof cover for the purposes of Hazus wind vulnerability assignments for WMUH1-3, MMUH1-3, MERBL-M-H, MECBL-M-H, MLRI, MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO)

Choices: N/A, BUR, SPM

RoofQuality

Defines roof cover quality for the purposes of Hazus wind vulnerability assignments for WMUH1-3, MMUH1-3, MLRI

Choices: N/A, poor, good

RoofDeckAttachmentW

Defines Roof Deck Attachment (RDA) for wood for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MMUH1-3, MSF1-2, MLRM1, MLRM2

Choices: A, B, C, D

RDA-OWSJ

Defines Roof Deck Attachment (RDA) for OWSJ for the purposes of Hazus wind vulnerability assignments for MSF1-2

Choices: smtl standard, smtl superior, cshl standard, cshl superior

RoofToWallConnection

Defines Roof to Wall Connection (R2WC) for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MMUH1-3, MSF1-2, MLRM1, MLRM2

Choices: strap, toe-nail

Shutters

Defines use of window opening protection for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MMUH1-3, MSF1-2, MERBL-M-H, MECBL-M-H, MMUH1-3,MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H, SPMBS-M-L, MH94HUDI-II-III, MH76HUD, MHPHUD and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO)

Choices: yes, no

AugmentedGarage

Defines presence of attached garage for the purposes of Hazus wind vulnerability assignments for WSF1-2, MSF1-2

Choices: none, SFBC 1994, standard, weak

MasonryReinforcing

Defines presence of reinforcement in masonry walls for the purposes of Hazus wind vulnerability assignments for MSF1-2, MLRI, MLRM1, MLRM2, MMUH1-3

Choices: yes, no

OWSJ-r

Defines property of OWSJ required for Hazus wind vulnerability assignments for MSF1-2

Choices: cshl, smtl

RoofDeckAttachmentM

Defines Metal Roof Deck Attachment (RDA) for purposes of Hazus wind vulnerability assessments for MLRI, MERBL-M-H, MECBL-M-H, MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, SPMBS-M-L and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO)

Choices: standard, superior

RoofDeckAge

Defines roof deck age for the purposes of Hazus wind vulnerability assessments for MLRM1, MLRM2, SPMBS-M-L and Fire Stations (HUEFFS), Elementary Schools (HUEFSS)

Choices: new/avg, old

UnitClass

Defines number of units in strip mall for purposes of Hazus wind vulnerability assessments for MLRM2

Choices: single, multi

JoistSpace

Defines joist spacing for multi-unit strip malls for purposes of Hazus wind vulnerability assessments for MLRM2

Choices: N/A, 4, 6

WindDebris

Defines likely sources of wind debris for purpose of Hazus wind vulnerability assessments for MERBL-M-H, MECBL-M-H, MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO)

Choices: Res/Comm, Varies by Direction, Residential, None, A, B, C, D

WindowAreaRatio

Defines window to wall ratio (WWR) for purpose of Hazus wind vulnerability assessments for MERBL-M-H, MECBL-M-H, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO)

Choices: low, medium, high

TieDowns

Defines use of ties to connect mobile homes to foundations per HUD guidelnes for purpose of Hazus wind vulnerability assessments for MH94HUDI-II-III, MH76HUD, MHPHUD

Choices: yes, no

HazusClass-IN

Hazus building classes as defined for inundation (flooding)

Choices: SF1XA, SF1XV, SF2XA, SF2XV, SF2BA, SF2BV, SF2SA, SF2SV, MH, APT, HOT, NURSE, RETAIL, WHOLE, SERVICE, OFFICE, BANK, HOSP, MED, REC, THEAT, GARAGE, INDH, INDL, CHEM, PROC, CONST, AGRI, RELIG, CITY, EMERG, SCHOOL

HazusClass-WA

Hazus building classes as defined for wave action

Choices: W1, W2, W3, MC1, MC2, MC3, S1, S2, S3, MH

FloodType

Assignment to flood zones as defined for Hazus damage/loss descriptions

Choices: Riverine/A-Zone, Coastal/A-Zone, Coastal/V-Zone

FirstFloorElev

Assignment of first floor height as defined by Hazus

Floating Point Number

PostFIRM

Assignment of FIRM phasing as defined by Hazus

Choices: Yes, No

NumberofStories

Initalizing number of stories for Hazus analysis

integer

BasementType

Assignement of basement type for Hazus analysis

Choices: Basement, Split-Level Basement, No Basement

OccupancyType

Assignment of Occupancy type for Hazus analysis

Choices: SF1XA, SF1XV, SF2XA, SF2XV, SF2BA, SF2BV, SF2SA, SF2SV, MH, APT, HOT, NURSE, RETAIL, WHOLE, SERVICE, OFFICE, BANK, HOSP, MED, REC, THEAT, GARAGE, INDH, INDL, CHEM, PROC, CONST, AGRI, RELIG, CITY, EMERG, SCHOOL

Duration

Assignment of storm suration for Hazus Analysis

Short, Long

WaveVelocity

Definition of wave velocity in ft/s for Hazus Analysis

Floating Point Number

2.2.2. Phase II: Inventory Generation

This section describes how the large-scale building inventory was constructed for Atlantic County using a phased approach that used machine learning, computer vision algorithm and data distributions to generate all attributes required for the corresponding loss assessment. It is emphasized that the intent is to demonstrate how an inventory could be constructed and not to address potential errors, omissions or inaccuracies in the source data, i.e., source data are assumed to be accurate, and no additional quality assurance was conducted outside of addressing glaring omissions or errors.

For each of the attributes identified in Table 2.2.1.1, a description of the attribute and information on how the data was identified and validated is presented.

Phase II: Footprint Selection

Inventory development began with the footprint Data generated by the New Jersey Department of Environmental Protection (NJDEP). These NJDEP footprints include flood-exposed properties cataloged in two geodatabases encompassing approximately 453,000 footprints across the entire state:

1. BF_NJDEP_20190612: all building footprints within 1% annual chance (AC) floodplain, as defined by FEMA Flood Insurance Rate Maps (FIRMs).

2. 02pct_20190520 Building_Footprints_02pct: buildings that are not in the first dataset but fall within a 200-ft buffer of the 1% AC floodplain boundary.

These databases were then combined, with only properties within the limits of Atlantic County retained to form the Flood-Exposed Inventory. This inventory was then extended to include other footprints within the county boundaries. Microsoft (MS) Footprint Database was utilized as the primary source of Non-NJDEP footprint polygons. One observed shortcoming of the MS Footprint Database is it incorrectly lumps together the footprints of closely spaced buildings. This issue was resolved by a combination of manual inspections and applying a separate roof segmentation algorithm to the satellite images obtained for the buildings. This resulted in the Atlantic County Inventory.

AI/ML Techniques combined with Computer Vision

Most of the key building attributes were generated using SimCenter’s BRAILS. The following

is a brief description of how these attributes were obtained and the utilized methods were validated.

Attribute: NumberOfStories

Number of floors of each building is determined using an object detection procedure implemented in BRAILS. A detection model that can automatically detect rows of building windows was established to generate the image-based detections of visible floor locations from street-level images. The model was trained on the EfficientDet-D7 architecture with a dataset of 60,000 images, using 80% for training, 15% for validation, and 5% testing of the model. In order to ensure faster model convergence, initial weights of the model were set to model weights of the (pretrained) object detection model that, at the time, achieved state-of-the-art performance on the 2017 COCO Detection set. For this specific implementation, the peak model performance was achieved using the Adam optimizer at a learning rate of 0.0001 (batch size: 2), after 50 epochs. Fig. 3.2.2.5 shows examples of the floor detections performed by the model.

../../../_images/NumOfStoriesDetection.png

Fig. 2.2.2.1 Sample floor detections of the floor detection model (each detection is indicated by a green bounding box). The percentage value shown on the top right corner of a bounding box indicates model confidence level associated with that prediction.

For an image, the described floor detection model generates the bounding box output for its detections and calculates the confidence level associated with each detection (see Fig. 3.2.2.5). A post-processor that converts stacks of neighboring bounding boxes into floor counts was developed to convert this output into floor counts. Recognizing an image may contain multiple buildings at a time, this post-processor was designed to perform counts at the individual building level.

For a random image dataset of buildings captured using arbitrary camera orientations (also termed in the wild images), the developed floor detection model was determined to capture the number of floors information of buildings with an accuracy of 86%. Fig. 3.2.2.6 (a) provides a breakdown of this accuracy measure for different prediction classes (i.e. the confusion matrix of model classifications). It was also observed that if the image dataset is established such that building images are captured with minimal obstructions, the building is at the center of the image, and perspective distortions are limited, the number of floors detections were performed at an accuracy level of 94.7% by the model. Fig. 3.2.2.6 (b) shows the confusion matrix for the model predicting on the “cleaned” image data. In quantifying both accuracy levels, a test set of 3,000 images randomly selected across all counties of a companion testbed in New Jersey, excluding Atlantic County (site of that testbed), was utilized.

../../../_images/NumOfStoriesVali.png

Fig. 2.2.2.2 Confusion matrices for the number of floors predictor used in this study.

Attribute: MeanRoofHt

The elevation of the bottom plane of the roof (lowest edge of roof line) and elevation of the roof (peak of gable or apex of hip) are estimated with respect to grade (in feet) from street-level imagery. These geometric properties are defined visually for common residential coastal typologies in Fig. 3.2.2.7. The mean height of the roof system is then derived as the average of these dimensions.

../../../_images/BldgElev.png

Fig. 2.2.2.3 Schematics demonstrating elevation quantities for different foundation systems common in coastal areas.

The MeanRoofHt is based on the following AI technique. Fig. 3.2.2.8 plots the predicted roof height versus the number of floors of the inventory.

As in any single-image metrology application, extracting the building elevations from imagery requires:

  1. Rectification of image perspective distortions, typically introduced during capturing of an image capture.

  2. Determining the pixel counts representing the distances between ends of the objects or surfaces of interest (e.g., for first-floor height, the orthogonal distance between the ground and first-floor levels).

  3. Converting these pixel counts to real-world dimensions by matching a reference measurement with the corresponding pixel count.

Given that the number of street-level images available for a building can be limited and sparsely spaced, a single image rectification approach was deemed most applicable for regional-scale inventory development. The first step in image rectification requires detecting line segments on the front face of the building. This is performed by using the L-CNN end-to-end wireframe parsing method. Once the segments are detected, vertical and horizontal lines on the front face of the building are automatically detected using RANSAC line fitting based on the assumptions that line segments on this face are the predominant source of line segments in the image and the orientation of these line segments change linearly with their horizontal or vertical position depending on their predominant orientation. The Another support vector model implemented for image rectification focuses on the street-facing plane of the building in an image, and, based on the Manhattan World assumption, (i.e., all surfaces in the world are aligned with two horizontal and one vertical dominant directions) iteratively transforms the image such that horizontal edges on the facade plain lie parallel to each other, and its vertical edges are orthogonal to the horizontal edges.

In order to automate the process of obtaining the pixel counts for the ground elevations, a facade segmentation model was trained to automatically label ground, facade, door, window, and roof pixels in an image. The segmentation model was trained using DeepLabV3 architecture on a ResNet-101 backbone, pretrained on PASCAL VOC 2012 segmentation dataset, using a facade segmentation dataset of 30,000 images supplemented with relevant portions of ADE20K segmentation dataset. The peak model performance was attained using the Adam optimizer at a learning rate of 0.001 (batch size: 4), after 40 epochs. The conversion between pixel dimensions and real-world dimensions were attained by use of field of view and camera distance information collected for each street-level imagery.

Fig. 3.2.2.8 shows a scatter plot of the AI predicted mean roof heights vs AI-predicted number of floors. A general trend observed in this plot is that the roof height increases with the number of floors, which is in line with the general intuition.

../../../_images/MeanRoofHtApp.png

Fig. 2.2.2.4 AI-predicted MeanRoofHt versus number of floors.

Attribute: FirstFloorElevation

FirstFloorElevation

Attribute: RoofSlope

RoofSlope is calculated as the ratio between the roof height and the roof run. Roof height is obtained by determining the difference between the bottom plane and apex elevations of the roof as defined in the Attribute: MeanRoofHt section. Roof run is determined as half the smaller dimension of the building, as determined from the dimensions of the building footprint. Fig. 3.2.2.9 displays the AI-predicted mean roof height versus the AI-precited roof pitch ratios. As expected, very little correlation between these two parameters are observed.

../../../_images/RoofSlopeApp.png

Fig. 2.2.2.5 AI-predicted RoofSlope versus mean roof height.

Attribute: RoofShape

The RoofShape is obtained by CityBuilder using the BRAILS Roof shape module. The roof shape module determines roof shape based on a satellite image obtained for the building. The module uses machine learning, specifically it utilizes a convolutional neural network that has been trained on satellite images. In AI/ML terminology the Roof Shape module is an image classifier: it takes an image and classifies it into one of three categories used in HAZUS: gable, hip, or flat as shown in Fig. 3.2.2.1. The original training of the AI model utilized 6,000 images obtained from google satellite imagery in conjunction with roof labels obtained from Open Street Maps. As many roofs have more complex shapes, a similitude measure is used to determine which of these roof geometries is the best match to a given roof. More details of the classifier can be found here. The trained classifier was employed here to classify the roof information for Lake Charles.

../../../_images/RoofShape.png

Fig. 2.2.2.6 Roof type classification with examples of aerial images (a-f) and simplified archetypes (d-f) used by Hazus.

The performance of the roof shape classifier was validated against two ground truth datasets. The first is comprised of 125 manually labeled satellite images sampled from OpenStreetMap from across the US, retaining only those with unobstructed views of building roofs (a cleaned dataset). The second is 56 residences assessed by StEER for which roof types were one of the three HAZUS classes, e.g., removing all roofs labeled as “Complex” according to StEER’s distinct image labeling standards. The validation process is documented here. The confusion matrices are presented in Fig. 3.2.2.2. These matrices visually present the comparison between the predictions and actual data and should have values of 1.0 along the diagonal if the classification is perfect, affirming the accuracy of the classification by the roof shape classifier.

../../../_images/RoofShapeVali.png

Fig. 2.2.2.7 Validation of BRAILS predicted roof shapes to roof shapes from OpenStreetMap and StEER.

Attribute: RoofSlope

RoofSlope is calculated as the ratio between the roof height and the roof run. Roof height is obtained by determining the difference between the bottom plane and apex elevations of the roof as defined in the Attribute: MeanRoofHt section. Roof run is determined as half the smaller dimension of the building, as determined from the dimensions of the building footprint. Fig. 3.2.2.9 displays the AI-predicted mean roof height versus the AI-precited roof pitch ratios. As expected, very little correlation between these two parameters are observed.

../../../_images/RoofSlopeApp.png

Fig. 2.2.2.8 AI-predicted RoofSlope versus mean roof height.

2.2.3. Phase III: Augmentation Using Third-Party Data

Attributes were then parsed from third-party data providers to populate all required attributes in the Building Inventory data model. For the Flood-Exposed Inventory, NJDEP had already enriched these footprints with various attributes necessary to conduct standard FEMA risk assessments. Specifically, all footprints included a set of Basic Attributes (Table 2.2.3.1). A subset of the data, including Atlantic County, had additional Advanced Attributes required by HAZUS User Defined Facilities (UDF) Module (Table 2.2.3.2) and FEMA Substantial Damage Estimator (SDE) Tool (Table 2.2.3.3).

Table 2.2.3.1 NJDEP basic attributes available for all properties in Flood-Exposed Inventory.

Field

Description

BldgUniqueID

Building unique ID. The first four characters will be followed by a 9-digit zero-padded number.

ParcelID

ParcelID from MOD-IV.

LAG

Lowest Adjacent Grade.

HAG

Highest Adjacent Grade.

AreaSqFt

Area square feet.

PamsPIN

MunicipalityCode_Block_Lot_Qual.

CentroidX

X coordinate in State Plane Feet.

CentroidY

Y coordinate in State Plane Feet.

DemoYear

Year demolished.

BldgNum

Unique ID for multiple structures in one lot (PAMS_PIN).

BldgType

Tag with domain name (i.e., Apartment, Condo).

Elevation

Numeric value.

ElevUnits

Units of elevation (i.e., feet).

County

County name

Table 2.2.3.2 Advanced attributes for HAZUS User Defined Facilities (UDF), available for all properties in Flood-Exposed Inventory.

Field

Description

BldgUniqueID

Building unique ID. The first four characters will be followed by a 9-digit zero-padded number.

Name

Typically assessor attribute for owner.

Address

Typically assessor field for property location.

City

Typically assessor field for property location city.

State

Typically assessor field for property location - state abbreviation.

ZipCode

Typically assessor field for property location Zip Code.

Contact

Typically assessor attribute for owner.

Tract

Value for Census Tract or an area roughly equal to a neighborhood established by the Census Bureau.

PhoneNumber

Typically assessor attribute for owner.

OccupancyClass

Residential, Commercial, Industrial, Agriculture, Government, Education, and Religious/Non-Profit.

BuildingType

Core construction material type; Wood, Concrete, Steel, Masonry, Manufactured Housing.

Cost

Replacement value; assessor data does not often include replacement cost. It is usually derived by considering heated or livable space and multiplied by cost per square foot.

YearBuilt

Typically assessor attribute.

Area

Heated or livable space. May or may not exist in typically assessor attributes. Can potentially be derived from building footprints.

NumberofStories

Typically assessor attribute. Indicates number of stories.

DesignLevel

Must have the year built to establish; see Hazus Flood Model User Manual, Table 6.2.

FoundationType

Flood model needs to distinguish which of seven types.

FirstFloorHt

Flood model needs height (in feet) above grade. Can be based on default values assigned to foundation types or other preferred methods.

ContentCost

Can be calculated from formula to be applied to final cost per Hazus Flood Model User Manual.

BldgDmgFnID

The damage function ID from Hazus would be entered in this field if anything other than the default was to be used. The damage function is based on the building characteristics defined in the items above.

ContDmgFnID

The damage function ID from Hazus would be entered in this field if anything other than the default was to be used. The damage function is based on the building characteristics defined in the items above.

InvDmgFnID

The damage function ID from Hazus would be entered in this field if anything other than the default was to be used. The damage function is based on the building characteristics defined in the items above.

FloodProtection

Does protection exist, and if yes to what frequency? (e.g., 100-year).

ShelterCapacity

Number of persons that can be sheltered.

BackupPower

Does backup power exist? [yes (1) or no (0)]

Latitude

Latitude of the Building Centroid (inside polygon).

Longitude

Longitude of the Building Centroid (inside polygon).

Comment

As needed.

InventoryCost

Can be calculated from formula to be applied to final cost per Hazus Flood Model User Manual.

RiverineCoastal

Can be used to identify if building is in riverine or coastal areas. Can be used to help designate the Damage Function IDs.

HzSourceCit

Source Citation details.

Table 2.2.3.3 Advanced attributes for FEMA Substantial Damage Estimator (SDE) Tool, available for all properties in Flood-Exposed Inventory.

Field

Description

BldgUniqueID

Building unique ID. The first four characters will be followed by a 9-digit zero-padded number.

StructureType

Residential or Non-Residential.

ResidenceType

Residential Type Only: Single Family Residence, Town or Row House, Manufactured House.

StructureUse

Non-Residential Type Only: Apartments, Commercial Retail, Mini-Warehouse, etc.

FoundationType

Residential Type Only: Select from domain.

SuperStructure

Residential Type Only: Select from domain.

ExteriorFinish

Residential Type Only: Select from domain.

ElevationLowestFloor

Typically assessor attribute.

NFIPCommunityID

Typically assessor attribute.

NFIPCommunityName

Typically assessor attribute.

YearConstruction

Typically assessor attribute.

Story

Select from domain.

RoofCovering

Select from domain.

HVACSystem

Select from domain.

Quality

Select from domain.

SDESourceCit

Source citation details.

FloodZone

From FIRM.

FIRMPanelID

FIRM Panel number from S_FIRM_PAN

For the Atlantic County Inventory, any building not included in the NJDEP footprints had attributes encompassed by NJDEP Basic, UDF or SDE fields assigned by parsing New Jersey Tax Assessor Data (called MODIV) ([MODIV]) as defined in the MODIV User Manual ([MODIV18]). This notably affected attributes such as OccupancyClass, BuildingType and FoundationType, whose rulesets (PDFs and Python scripts) are cross-referenced in Table 2.2.3.4. In all cases where attributes were derived from MODIV data, whose fields can be sparsely populated, default values were initially assigned to ensure that every footprint would have the attributes required to execute the workflow. These default values were selected using engineering judgment to represent the most common/likely attribute expected or conservatively from the perspective of anticipated losses (i.e., picking the more vulnerable attribute option). These initial assignments were then updated if additional data is available in MODIV to make a more faithful attribute assignment.

Table 2.2.3.4 Additional details for rulesets assigning attributes available only in NJDEP dataset

Ruleset Name

Ruleset Definition Table

Python script

Building Type Rulesets

Building Type Rulesets.pdf

To be released

Foundation Type Rulesets

Foundation Type Rulesets.pdf

To be released

Occupancy Type Rulesets

Occupancy Type Rulesets.pdf

To be released

Some attributes in the Building Inventory Data Model were not encompassed by NJDEP Basic, UDF or SDE fields, thus remaining attributes in both the Flood-Exposed and Atlantic County Inventories were assigned using data from the following third-party sources: 1. Locations of essential facilities were sourced from NJ Office of Information Technology (part of NJGIN Open Data [NJGIN20]) 2. ATC Hazards by Location API ([ATC20]) was used to query Design Wind Speeds as defined in ASCE 7 3. Terrain features (roughness length associated with different exposure classes) was derived from Land Use Land Cover data (part of NJGIN Open Data [NJGIN20])

See the Transformation and Detail columns in the PDFs listed in tab-bldgInventory for specifics of how each attribute was assigned using these various third-party data sources.

2.2.4. Phase IV: Augmentation Using Computer Vision Methods

A number of required attributes pertaining to externally-visible features of the building were either not included in the NJDEP footprints or MODIV data or were included but warranted cross validation. The methodology used for each of these attributes is now described.

  1. Number of Stories: While this attribute was available only for the buildings included in the NJDEP inventory,

    this attribute was sparsely reported in the MOD IV database. Even for the NJDEP inventory, non-integer values were often reported, creating incompatibilities with the integer defaults used in Hazus. Thus image-based floor detections were used to estimate this attribute for the larger Flood-Exposed Inventory, and as a means to cross-validate values reported in NJDEP and MOD IV for consistency with Hazus conventions.

    A detection model that can automatically detect rows of building windows was established to generate the image-based detections of visible floor locations from street-level images. The model was trained on the EfficientDet-D7 architecture with a dataset of 60,000 images, using 80% for training, 15% for validation, and 5% testing of the model. In order to ensure faster model convergence, initial weights of the model were set to model weights of the (pretrained) object detection model that, at the time, achieved state-of-the-art performance on the 2017 COCO Detection set. For this specific implementation, the peak model performance was achieved using the Adam optimizer at a learning rate of 0.0001 (batch size: 2), after 50 epochs. Fig. 3.2.2.5 shows examples of the floor detections performed by the model.

    ../../../_images/number_of_stories_detection.png

    Fig. 2.2.4.1 Sample floor detections of the floor detection model (each detection is indicated by a green bounding box). The percentage value shown on the top right corner of a bounding box indicates model confidence level associated with that prediction.

    For an image, the described floor detection model generates the bounding box output for its detections and calculates the confidence level associated with each detection (see Fig. 3.2.2.5). A post-processor that converts stacks of neighboring bounding boxes into floor counts was developed to convert this output into floor counts. Recognizing an image may contain multiple buildings at a time, this post-processor was designed to perform counts at the individual building level.

    For a random image dataset of buildings captured using arbitrary camera orientations (also termed in the wild images), the developed floor detection model was determined to capture the number of floors information of buildings with an accuracy of 86%. Fig. 3.2.2.6 (a) provides a breakdown of this accuracy measure for different prediction classes (i.e. the confusion matrix of model classifications). It was also observed that if the image dataset is established such that building images are captured with minimal obstructions, the building is at the center of the image, and perspective distortions are limited, the number of floors detections were performed at an accuracy level of 94.7% by the model. Fig. 3.2.2.6 (b) shows the confusion matrix for the model predicting on the “cleaned” image data. In quantifying both accuracy levels, a test set of 3,000 images randomly selected across all counties of a companion testbed in New Jersey, excluding Atlantic County (site of that testbed), was utilized.

../../../_images/NumOfStoriesVali.png
  1. Building Elevations: Building elevations are not available in state inventory data and required for both

    wind and flood loss modeling, with the exception of first floor height estimates provided in the NJDEP inventory. Hence, the elevation of the bottom plane of the roof (lowest edge of roof line), elevation of the roof (peak of gable or apex of hip), and height of first of floor as determined from base of door’s height, all defined with respect to grade (in feet), were estimated from street-level imagery. These geometric properties are defined visually for common residential coastal typologies in Fig. 3.2.2.7. The mean height of the roof system is then derived from the aforementioned roof elevations.

    ../../../_images/building_elevation.png

    Fig. 2.2.4.2 Schematics demonstrating elevation quantities for different foundation systems common in coastal areas.

    As in any single-image metrology application, extracting the building elevations require: 1. Rectification of image perspective distortions, typically introduced during image capture, 2. Determining the pixel count representing the distance between ends of the objects or surface of interest 1. (e.g., for first-floor height, the orthogonal distance between the ground and first-floor levels) 3. Converting these pixel counts to real-world dimensions by matching a reference measurement with the corresponding pixel count

    Given that the number of street-level images available for a building can be limited and sparsely spaced, this single image rectification approach was deemed most applicable for regional-scale inventory development. The first step in image rectification requires detecting line segments on the front face of the building. This is performed by using the L-CNN end-to-end wireframe parsing method. Once the segments are detected, vertical and horizontal lines on the front face of the building are automatically detected using RANSAC line fitting based on the assumptions that line segments on this face are the predominant source of line segments in the image and the orientation of these line segments change linearly with their horizontal or vertical position depending on their predominant orientation. Invoking the Manhattan World assumption (i.e., all surfaces in the world are aligned with two horizontal and one vertical dominant directions), we iteratively transform the image such that horizontal edges on the facade plain lie parallel to each other, and its vertical edges are orthogonal to the horizontal edges.

    In order to automate the process of obtaining the pixel counts for the ground elevations, a face segmentation model was trained to auotmatically label ground, facade, door, windwos and roof pixels in an image. The segmentation model was trained using DeepLabV3 architecture on a ResNet-101 backbone, pretrained on PASCAL VOC 2012 segmentation dataset, using a facade segmentation dataset of 30,000 images. The peak model performance was attained using the Adam optimizer at a learning rate of 0.001 (batch size: 4), after 40 epochs. The conversion between pixel dimensions and real-world dimensions were attained by use of edge detections performed on satellite images.

    The conversion between pixel dimensions and real-world dimensions were attained by use of edge detections performed on satellite images.

  2. Roof Geometry: Roof shape and slope are not available in state inventory data and required for wind loss

    modeling. The SimCenter developed application Building Recognition using Artificial Intelligence at Large Scales, BRAILS ([Wang19]), is used to interpret satellite images of building roofs, which are collected from Google Maps. The satellite images are labeled with shape types to form a dataset, upon which a Convolutional Neural Network (CNN) is trained so that it can give rapid predictions of roof types when given new images of roofs. The footprint centroid (Latitude and Longitude in Building Inventory) is used as the location index when downloading images automatically from Google Maps. While more complex roof shapes could in theory be classified, the current use of HAZUS damage and loss functions required the use of similitude measures to define each roof as an “effective” gable, hip or flat geometry (Fig. 3.2.2.1). Using BRAILS, this classification was achieved with approximately 90.3% accuracy based on validation studies. The detailed validation process can be found in BRAILS online documentation.

    ../../../_images/roof_shape.png

    Fig. 2.2.4.3 Roof type classification by BRAILS ([Wang19]).

    Roof slope is calculated as the ratio between the roof height and half the depth of the building, i.e., length of the building orthogonal to the roadline in front of the building. Roof height is calculated by determining the difference between the bottom plane and apex elevations of the roof as defined in the Building Elevations section. Plan dimensions of the building, as determined by the dimensions of the footprint, are determined by first obtaining the camera location of the street-level image to determine road-parallel and road-perpendicular dimensions of the building footprint, then setting the average of the road-perpendicular dimensions as the building depth. This is deemed a more accurate way of establishing the plan geometry than using the footprints themselves.

  3. Window Area: The proportion of windows to the overall surface area is not available in inventory and

    assessor datasets though required for wind loss modeling. Generally, window area can be assumed based on the building occupancy class given Department of Energy industry databases. This property can also be estimated from street-level imagery, by taking advantage of the window masks generated as part of the segmentation performed when determining building elevations. For this application, window area is defined as a percentage of the total facade area as the ratio of the area of windows masks to the area of the front facade of the building. The underlying assumption is that the proportion of surface area occupied by windows at the front of the building is representative of the amount of window openings on the sides and rear of the building. This enables the ratio calculated for the front face of the building to be used for the whole building. This assumption may hold for single family residential buildings, but possibly not for other commercial construction where street fronts have higher proportions of glass. In lieu of this computer vision approach, users may choose to adopt industry norms for their window areas (see callout box below).

    Note

    Industry Norms on Window Area: Engineered residential buildings can be assumed to have low window to wall area ratios (WWR) based on the information for Reference Buildings in Baltimore, MD from the Office of Energy Efficiency and Renewable Energy. Reference Buildings were created for select cities based on climate profile; of the available cities, Baltimore is selected since its climate is most similar to Atlantic City, NJ. Office buildings (used as a test case for commercial), have WWR of 33% and apartments (used as a test case for residential) have WWR of 15%.

Note

The process of constructing the Atlantic County Inventory for footprints beyond those in the Flood-Exposed Inventory underscored a number of tasks/issues that are commonly encountered when constructing an inventory in a location with sparse inventory data. Recommended best practices are summarized in Best Practices.

2.2.5. Populated Inventories

Executing this four-phase process resulted in the assignment of all required attributes at the asset description stage of the workflow for both the Atlantic County Inventory and the Flood-Exposed Inventory. Table 2.2.5.1 and Table 2.2.5.2 provide respective examples of each of these inventories. The Flood-Exposed Inventory then was used to extract out the subset of buildings defining the Exploration Inventory (see example in Table 2.2.5.3). The full inventories can be downloaded here.

Table 2.2.5.1 Illustrative sample of building in Atlantic Inventory.

Longitude

Latitude

Address

City

BldgClass

YearBuilt

NumberofStories1

OccupancyClass

BuildingType

-74.52

39.39

368 UPLAND AVE

ABSECON CITY

17

1986

1

RES1

3001

-74.69

39.65

368 UPLAND AVE

ABSECON CITY

17

1986

1

RES1

3001

-74.52

39.39

135 TENTH AVE

ABSECON CITY

17

1988

1

RES1

3001

-74.71

39.65

135 TENTH AVE

ABSECON CITY

17

1988

1

RES1

3004

-74.52

39.39

358 UPLAND AVE

ABSECON CITY

17

1986

1

REL1

3004

-74.71

39.65

358 UPLAND AVE

ABSECON CITY

17

1986

1

RES1

3001

-74.53

39.39

360 UPLAND AVE

ABSECON CITY

17

1986

1

RES1

3001

-74.78

39.64

360 UPLAND AVE

ABSECON CITY

17

1986

1

RES1

3001

-74.53

39.39

362 UPLAND AVE

ABSECON CITY

17

1986

1

RES1

3001

Table 2.2.5.2 Illustrative sample of building in Flood-Exposed Inventory.

Address

City

State

Latitude

Longitude

OccupancyClass

BuildingType

UseCode

BldgClass

DesignLevel

YearBuiltNJDEP

YearBuiltMODIV

NumberofStories0

NumberofStories1

NoUnits

PlanArea0

PlanArea1

FoundationType

SplitLevel

ElevationR0

ElevationR1

FirstFloorHt0

FirstFloorHt1

FloodZone

DSWII

WindZone

AvgJanTemp

RoofShape

RoofSlope

RoofCover

RoofSystem

MeanRoofHt

WindowArea

Garage

z0

14 W LEE AVE

Absecon

NJ

39.42

-74.5

RES1

3001

17

NE

1956

1956

3101

2

1

1311

1311

3505

NO

19.64

33.89

3

-2.97

6112

122

I

Above

Gable

0

5701

26.8

0

0

0.35

28 W SUMMIT AVE

Absecon

NJ

39.42

-74.5

RES1

3001

17

NE

1948

1948

3101

2

1

1061

1061

3505

NO

33.52

47.46

3

6.72

6112

122

I

Above

Gable

0

5701

40.5

0

0

0.35

6353 MONMOUTH DRIVE

Hamilton

NJ

39.45

-74.75

RES1

3001

17

NE

1954

1954

3101

1

1

1047

1047

3507

NO

18.92

31.75

1

2.02

6112

118

I

Above

Gable

0

5701

25.3

0

0

0.35

2004 SYCAMORE LANE

Hamilton

NJ

39.53

-74.79

RES1

3001

17

NE

1977

1977

3102

2

1

1234

1234

3507

NO

31.43

43.06

1

1.96

6112

117

I

Above

Hip

0

5701

37.2

0

1.1

0.35

35 PENNINGTON AVENUE

Hamilton

NJ

39.45

-74.73

RES1

3001

17

NE

1924

1924

3103

2

1

936

936

3505

NO

34.55

47.39

3

6.76

6112

119

I

Above

Gable

0

5701

41

0

0

0.35

135 GARNETT LANE

Egg Harbor

NJ

39.38

-74.59

RES1

3001

16

NE

2005

2005

3102

2

1

2579

2579

3507

NO

29

37.43

1

2.6

6112

121

I

Above

Gable

0

5701

33.2

0

0

0.35

122 GARNETT LANE

Egg Harbor

NJ

39.38

-74.59

RES1

3001

16

NE

2005

2005

3102

2

1

2441

2441

3507

NO

27.38

33.19

1

0.27

6112

121

I

Above

Hip

0

5701

30.3

0

0

0.35

332 MONTCLAIR DR

Pleasantville

NJ

39.41

-74.5

RES3C

3001

16

E

1954

1954

3303

1

1

1602

1602

3507

NO

16.67

24.08

1

0.87

6112

122

I

Above

Gable

0

20.4

0

1.1

0.35

128 N FRANKLIN BLVD

Pleasantville

NJ

39.39

-74.52

RES1

3001

15

NE

1943

1943

3101

2

1

1062

1062

3505

NO

32.89

42.43

3

8.08

6112

122

I

Above

Hip

0

5701

37.7

0

0

0.35

Table 2.2.5.3 Illustrative sample of building in Exploration Inventory.

Address

City

State

Latitude

Longitude

OccupancyClass

BuildingType

UseCode

BldgClass

DesignLevel

YearBuiltNJDEP

YearBuiltMODIV

NumberofStories0

NumberofStories1

NoUnits

PlanArea0

PlanArea1

FoundationType

SplitLevel

ElevationR0

ElevationR1

FirstFloorHt0

FirstFloorHt1

FloodZone

DSWII

WindZone

AvgJanTemp

RoofShape

RoofSlope

RoofCover

RoofSystem

MeanRoofHt

WindowArea

Garage

z0

425 N MARYLAND AVE

Atlantic City

NJ

39.37

-74.43

RES1

3001

210

NaN

NE

1969

0

3102

1

1

906.2940373

906.2940373

3505

NO

12.09

25.16

3

-0.83

6106

124

I

Above

Flat

0

5701

Wood

18.6

0

0

0.03

125 N FLORIDA AVE

Atlantic City

NJ

39.36

-74.44

RES3B

3001

NaN

ME

1969

0

3301

2

1

746.77695

746.77695

3505

NO

20.8

28.38

3

-1.12

6106

124

I

Above

Flat

0

NaN

Wood

24.6

0

0

0.03

600 HURON AVE

Atlantic City

NJ

39.38

-74.43

GOV2

3001

NaN

E

1969

0

3401

3

1

1513.12497

1513.12497

3507

NO

53.51

64.92

1

2.61

6102

124

I

Above

Hip

0

NaN

Wood

59.2

0

0

0.03

332 N RHODE ISLAND AVE

Atlantic City

NJ

39.37

-74.42

IND1

3003

100

NaN

E

1969

0

3401

1

1

1000.334181

1000.334181

3507

NO

12.94

25.08

1

0.65

6106

124

I

Above

Flat

0

NaN

Wood

19

0

0

0.03

1 ATLANTIC OCEAN

Atlantic City

NJ

39.35

-74.43

COM8

3003

738

NaN

ME

1969

0

3401

1

1

177963.6629

177963.6629

3502

NO

23.82

31.38

5

9.26

6102

124

I

Above

Flat

0

NaN

Wood

27.6

0

0

0.03

11 N MORRIS AVE

Atlantic City

NJ

39.35

-74.45

RES3B

3001

45

ME

1900

1900

3301

2

3

835.133255

835.133255

3504

NO

22.28

31.19

-4

0

6106

124

I

Above

Hip

0

NaN

Wood

26.7

0

0

0.03

601 N ALBANY AVE

Atlantic City

NJ

39.36

-74.46

COM1

3003

NaN

ME

1969

0

3401

1

1

654.0515616

654.0515616

3507

NO

12.92

22.85

1

0.48

6106

124

I

Above

Flat

0

NaN

Wood

17.9

0

0

0.03

1500-1538 ADRIATIC AVE

Atlantic City

NJ

39.37

-74.43

RES3C

3004

NaN

ME

1969

0

3301

1

1

938.5675163

938.5675163

3505

NO

22.15

30.29

3

8.37

6106

124

I

Above

Gable

0

NaN

Wood

26.2

0

0

0.03

900 BEACH THOROFARE

Atlantic City

NJ

39.38

-74.42

GOV2

3001

NaN

E

1969

0

3401

2

1

606.967372

606.967372

3507

NO

36.33

45.23

1

0.84

6106

124

I

Above

Flat

0

NaN

Wood

40.8

0

0

0.03

Table 2.2.5.4 Summary of the three building inventories.

Inventory Name

DesignSafe Document

Number of Assets

Typical Run Time

Atlantic County Inventory

Atlantic County Inventory

100,721

~ 1,500 CPU-Hour

Flood-Exposed Inventory

Flood-Exposed Inventory

32,828

~ 440 CPU-Hour

Exploration Inventory

Exploration Inventory

1,000

~ 12 CPU-Hour

ATC20

ATC (2020b), ATC Hazards By Location, https://hazards.atcouncil.org/, Applied Technology Council, Redwood City, CA.

NJGIN20(1,2)

NJ Geographic Information Network, State of New Jersey, https://njgin.nj.gov/njgin/#!/

Wang19(1,2)

Wang C. (2019), NHERI-SimCenter/SURF: v0.2.0 (Version v0.2.0). Zenodo. http://doi.org/10.5281/zenodo.3463676

Microsoft2018

Microsoft (2018) US Building Footprints. https://github.com/Microsoft/USBuildingFootprints

MODIV

Parcels and MOD-IV of Atlantic County, NJ. NJGIN Open Data, https://njogis-newjersey.opendata.arcgis.com/datasets/680b02ff9b4348409a2f4ccd4c238215.

MODIV18

Department of the Treasury, State of New Jersey (2018), MOD IV User Manual. https://www.state.nj.us/treasury/taxation/pdf/lpt/modIVmanual.pdf