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.

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.

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.

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.

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

Attribute: FirstFloorElevation

FirstFloorElevation is calculated using the distance between the bottom of the facade mask and the bottom of the fron door of the building. The process of obtaining these masks follow the same approach used for roof mean height and is not repeated here.

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.

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 Atlantic County.

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.

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.

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

Attribute: WindowArea

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.

2.2.3. 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. 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 2.2.4.1 and Table 2.2.4.2 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 2.2.4.3). The full inventories can be downloaded here.

Table 2.2.4.1 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 2.2.4.2 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 2.2.4.3 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 2.2.4.4 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

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