Nearmap AI customers can subscribe to any of the available AI Packs. This is allows you to view specific AI Layer attributes and export the AI Parcel data, both from within MapBrowser.
Currently available AI Packs are:
|AI Pack||AI Layers Included||AI Parcel Data Included*|
|Construction Site||Construction presence in a parcel (area estimate and centroid refer to parcel).|
|Swimming Pool||Swimming pool presence; area estimate and centroid of each pool.|
|Building||Building Footprint presence; area estimate and polygon for each building footprint.|
|Residential Roof Characteristics|
Roof Material (Tile, Metal, Shingle)
Roof Shape (Hip, Turret, Flat, Gable)
Attributes of each Building Footprint: Dominant Roof Material, presence of each roof shape.
Tree Overhang presence; area estimate and polygon of each element of overhang.
|Solar Panel||Solar panel presence; area estimate and centroid of each solar array.|
|Trampoline||Trampoline presence; area estimate and centroid of each trampoline.|
*All data are based on WGS84 (EPSG:4326)
**If you subscribe to Residential Roof characteristics you must also subscribe to Residential Building Footprints.
The table below is a summary of the statistical performance for each attribute with a "Y"/"N" flag in the spreadsheet output of an AI Parcel export. These numbers refer to whether at least one object of this type is present in an AI Parcel. The AI Packs also define what data will be delivered if you order a bulk offline delivery of data.
To ensure that our performance scores are as objective as possible, our examples are drawn from a statistically determined sample across our coverage regions that is weighted towards populated areas. We have deliberately chosen a significant portion of our examples as challenging cases where our models are least certain.
Our "source of truth" is a highly trained team of human expert labellers, who use a custom version of MapBrowser to check multiple dates, multiple angles, and even our 3D models to determine whether they believe an object is present. Our brief is "on the assessment date, mark what you judge to be present using all MapBrowser tools available to you". This means that a swimming pool missed on a leaf-on survey will be marked as incorrect, if the labeller can see the pool before or after that point in our capture history.
As a customer, you will notice three practical causes of error that stand out above all others:
- Inconsistencies in third party parcel boundaries, causing an object to be misidentified as belonging to a neighbouring property.
- Definitional differences, where your working definition is subtly different from ours (such as our current Building Footprint definition excluding rooftop carparks).
- What we like to term "forgivable errors" where, on clicking the MapBrowser link provided with every AI Parcel output, you may think to yourself "I appreciate why that would have happened". This is most noticeable for the Construction Site class, where the Precision is decreased by picking up things such as landscaper's yards full of rubble and trucks, and the Recall is decreased mostly by examples of the first stage of construction, which is usually just an area of dirt with no obvious construction occurring.
Our models do of course make other kinds of errors; however this is one of the primary reasons we have provided you with the ability to view AI Layers in Map Browser. Nearmap AI gives you a unique perspective of the truth on the ground based on visual data, with different strengths and weaknesses to other sources (such as paper records of construction or solar installations). The consistent behaviour makes it an excellent source of truth as a standalone data product. If you have an existing data set representing a different perspective, we encourage you to combine it with ours to achieve a much more accurate picture than either perspective could alone.
We are correct approximately <Precision %> of the time, in cases where we said "Yes". For example, we are correct 98.5% of the time in cases where we said "Yes, this is a swimming pool".
Precision is about whether the model accidentally flags parcels as "Yes". e.g. if we incorrectly flag a landscaper's yard as construction, it reduces the Precision.
We find approximately <Recall %> percent of the parcels which should actually say "Yes". For example we find approximately 92% of the parcels containing a swimming pool which actually do contain a swimming pool.
Recall is about whether the model accidentally misses parcels which should be picked up as "Yes". e.g. if we miss flagging a swimming pool because it is hidden completely under trees on the assessed imagery date, it reduces the Recall.
Precision and recall summary by attribute
|Class (object presence in a parcel, or on a building)|
|Dominant Roof Material||Tile Roof||96%||95.7%|
* 2/3 of the examples of missed turrets in the test set were partial round turrets (see AI Pack: Residential Roof Characteristics).
**The majority of missed construction is bare earth and slab down, before obvious signs of construction have commenced. The majority of false positive construction is "construction like" such as landscaper yards, or parcels with a lot of rubble and exposed dirt.
***The majority of missed swimming pools are very small above ground pools in a poor state of repair, or with a winter cover on.
****The majority of false positive solar panels are solar hot water systems, and easily excluded by cutting out lower confidence examples.
We help you make sense of the data and how you can use it by giving each attribute a Confidence score. This score is a representation of how much we believe the classification to be true.
Our confidence at detecting the different attributes varies, as some are more challenging than others.
US "regional" confidence values
The confidence results for Australia and each key US region are included in the discussion of each attribute for each AI Pack. The US regions are shown in the map below.
These results are presented graphically, like the sample below, with the highest confidence bands being the darkest colour.
Jump directly to the confidence discussion for each AI Attribute using the links below:
Minimum Object Sizes
The table below defines the minimum attribute area. Any object smaller than this mimimum area is excluded form the AI data set.
AI Parcel Data Specifications
This represents a summary of information, where each row is a property parcel, and columns are summarised facts about that parcel. It often contains less information than the geospatial data, but is very convenient to use if you do not have a GIS background. We often refer to it as a "parcel rollup", because it rolls up information about all objects detected in that AI Parcel to a single row in a database, a little like a geospatial version of a "GROUP BY" aggregation over parcels.
Most of the .csv files (comma separated value files) have common elements:
Common elements of .csv files
This applies to Trampoline.csv, Swimming_Pool.csv, Solar_Panel.csv, Construction_Site.csv
|property_id||Unique parcel identifier from third party provider that best matches the parcel|
GNAF (AU) / Parcel APN (US)
|survey_date||Capture date of imagery used for the object detection||DD/MM/YYYY|
Address data as supplied by third party that best matches the parcel
|longitude||Longitude coordinate of parcel centroid (EPSG:4326 WGS84)|
|latitude||Latitude coordinate of parcel centroid (EPSG:4326 WGS84)|
|attribute||Name of the attribute represented by the file, e.g. "Trampoline"|
|present||Yes/No value representing whether at least one example of the object has been detected "Y" or there has been insufficient evidence that any examples exist in the parcel "N".||Y/N|
A percentage measure (represented as e.g. "100%" in the file) of the likelihood we believe the "present" decision is correct.
"Y" cases are typically well calibrated, such that the decision should be correct in 9/10 cases among all examples with a confidence of 90%.
Note that "N" cases are typically set to 100% confidence for technical reasons (when there is no evidence at all of object presence, it is not possible to separate out different confidence levels meaningfully. 100% is approximately well calibrated, given the true negative rates are typically in excess of 99%).
An estimate of the total summed area of all instances of this type within the parcel, e.g. aggregate all sections of solar panel array. Units are selected automatically based on region, and reflected in the column name. Given the variation in areas due to occlusion from tree overhang etc., no statistical guarantees are made about the accuracy of the areas. Observing the AI Layers give a good visual indication of how well the area of each object is likely to be captured.
Estimated area is in the horizontal plane and does not take slope and 3D structure into account.
For Construction_Site.csv, the area represents the area of the parcel, not the area of the objects.
area_estimate_sqm - m2 (AU)
area_estimate_sqft - ft2 (US)
|map_browser||Direct link to the parcel centroid at the specified survey date in MapBrowser. This can be used for examining why a particular decision may have been made.|
|wkt||Well Known Text representation of parcel centroid for easy use within GIS software.|
Additional elements of .csv files
Some files have the above columns, plus additional ones representing further information. These include:
|building_confidence||Represents the likelihood that the "present" field is correct for "Roof", indicating the likelihood that there is at least one building present in the parcel.|
|dom_roof_material||Text field set to: "Tile", "Shingle", "Metal", "Other" or "NA"|
The Dominant Roof Material of the primary (largest) 'Roof/Building' within the parcel. "NA" is indicated when no buildings at all are detected in the parcel.
|dom_roof_material_confidence||The percentage likelihood that the dominant roof material has been chosen correctly.|
|roof_hip||As per "present" field, applied to hip elements on the largest roof in the parcel.|
|roof_hip_confidence||As per "confidence" field, applied to roof_hip.|
|roof_gable||As per "present" field, applied to gable elements on the largest building in the parcel.|
|roof_gable_confidence||As per "confidence" field, applied to roof_gable.|
|roof_flat||As per "present" field, applied to flat elements on the largest roof in the parcel.|
|roof_flat_confidence||As per "confidence" field, applied to roof_flat.|
|roof_turret||As per "present" field, applied to turret elements on the largest roof in the parcel.|
|roof_turret_confidence||As per "confidence" field, applied to roof_turret.|
|tree_overhang||As per "present" field, applied to presence of non-zero area of tree overhang on the largest building in the parcel.|
|tree_overhang_confidence||As per "confidence" field, applied to tree_overhang.|
As per "area_estimate", except referring to the summed area of tree overhangs on the largest building in the parcel.
Rich Geospatial Data
The GeoPackage files provided contain all the information in the spreadsheet files, but have some key differences, and represent more fine grained information about geospatial positions, areas and shapes of individual objects. It has been tested to open in QGIS, Esri's suite of ArcGIS products, and using the GeoPandas python package.
The geopackage data are all based on WGS84 (EPSG:4326).
- The point represents the centroid of the object rather than the centroid of the parcel.
- The area estimate represents the area estimate of that object (not the aggregated area of the object type within the parcel).
If multiple objects are present in a parcel, multiple points per parcel may be returned, each with their own area estimates. For Solar Panels, each group of panels on a roof is typically represented as a separate object. This applies to (Trampoline.gpkg, Swimming_Pool.gpkg, Solar_Panel.gpkg). Construction_Site.gpkg is the exception, where the point always represents the parcel centroid.
If only the "Residential Building Footprint" AI Pack is enabled, the file Building.gpkg will be present. The polygons represent every Building Footprint detected in each parcel.
If the "Residential Building Characteristics" AI Pack is also enabled, this file is replaced by Building_Footprint_With_Roof_Characteristic.gpkg. It contains the same Building Footprint polygons, with additional metadata for dominant roof material and roof shapes of each building. In that case, the file "Tree_Overhang.gpkg" will also be present, which contains polygons of all overhanging sections of tree detected.