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ExportFeatures MS_BuildingFootprints_2022 U:\HolliG\TEMP_StatewideData\NRCS_StatewideData.gdb\MS_BuildingFootprints_2022 # NOT_USE_ALIAS "release "release" true true false 1 Short 0 1,First,#,MS_BuildingFootprints_2022,release,-1,-1;capture_da "capture_da" true true false 21 Text 0 0,First,#,MS_BuildingFootprints_2022,capture_da,0,21" #
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ISO19139
Steve Walker
MARIS
GIS Operations Manager
Jackson
MS
39211
swalker@mississippi.edu
US
601 432-6149
M-f 7-3 CDT
Steve Walker
Steve Walker
meters
224.220
File Geodatabase Feature Class
2018-07-10T00:00:00
Steve Walker
MARIS
GIS Operations Manager
Jackson
MS
39211
swalker@mississippi.edu
US
601 432-6149
M-f 7-3 CDT
Steve Walker
MS_BuildingFootprints_2022
Building footprint polygons of Mississippi structures. This data was generated from BING raster imagery by Microsoft. MARIS (in October 2022), downloaded and converted the Mississippi file from JSON format to a WGS84 shapefile using Mapshaper software. Next, MARIS projected the shapefile into MSTM projection. There are 1,507,496 polygons in this layer which is 36,312 more than the 2018 release. Date on update is April,2022. Most of the state is from the 2018 release. Updates were mostly made in Southwest MS and the Coast between 2018 and 2021. Some other buildings were added in metro areas.
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-size:14pt">MS building footprint polygons - 2022</SPAN></P><P STYLE="font-weight:bold;margin:0 0 0 0;"><SPAN><SPAN>***** See original source download and supplemental Information for details on data creation by Microsoft.</SPAN></SPAN></P><P><SPAN /><A href="https://github.com/Microsoft/USBuildingFootprints"><SPAN><SPAN>https://github.com/Microsoft/USBuildingFootprints</SPAN></SPAN></A></P><P /><P><SPAN STYLE="font-weight:bold;">Microsoft explaination</SPAN><SPAN STYLE="font-style:italic;font-weight:bold;font-size:10pt">: </SPAN><SPAN STYLE="font-style:italic;font-size:9pt">“</SPAN><SPAN STYLE="font-style:italic;">The gap areas contain image tiles taken with different cameras, which is causing the creation of artificial edges between neighboring tiles. These confuse our detection network which hasn't learned to deal with them.We took a very conservative approach of skipping such tiles. I think we could add additional effort in order to properly deal with this problem.”</SPAN></P><P><SPAN /></P><P><SPAN /></P><P><SPAN /></P></DIV></DIV></DIV>
Microsoft, BING, MARIS
buildings
Mississippi
****** MICROSOFT SUPPLEMNTAL INFORMATON ********
What the data include?
129,591,852 building footprint polygon geometries divided by 50 US states and the District of Columbia in GeoJSON format.
Why is the data being released?
Microsoft has a continued interest in supporting a thriving OpenStreetMap ecosystem.
What is the GeoJson format?
GeoJSON is a format for encoding a variety of geographic data structures. For Intensive Documentation and Tutorials, Refer to GeoJson Blog.
Should we import the data into OpenStreetMap?
Maybe. Never overwrite the hard work of other contributors or blindly import data into OSM without first checking the local quality. While our metrics show that this data meets or exceeds the quality of hand-drawn building footprints, the data does vary in quality from place to place, between rural and urban, mountains and plains, and so on. Inspect quality locally and discuss an import plan with the community. Always follow the OSM import community guidelines.
Will the data be used or made available in larger OpenStreetMap ecosystem?
Yes. Currently Microsoft Open Buildings dataset is used in ml-enabler for task creation. You can try it out at AI assisted Tasking Manager. The data will also be made available in Facebook RapiD.
What is the creation process for this data?
The building extraction is done in two stages:
Semantic Segmentation – Recognizing building pixels on the aerial image using DNNs
Polygonization – Converting building pixel blobs into polygons
Stage1: Semantic Segmentation
Semantic Segmentation
DNN architecture and training
The network backbone we used is EfficientNet described here. Although we have millions of labels at our disposal, we found that an effective combination of supervised and unsupervised training yields the best results.
Stage 2: Polygonization
Polygonization
Method description
We developed a method that approximates the prediction pixels into polygons making decisions based on the whole prediction feature space. This is very different from standard approaches, e.g. the Douglas-Peucker algorithm, which are greedy in nature. The method tries to impose some of a priori building properties, which is, at the moment, manually defined and automatically tuned. Some of these a priori properties are:
How good is the data?
Our metrics show that in the vast majority of cases the quality is at least as good as data hand digitized buildings in OpenStreetMap.
DNN model metrics
These are the intermediate stage metrics we use to track DNN model improvements and they are pixel based. Pixel recall/precision = 95.5%/94.0%
Polygon evaluation metrics
Match metrics:
Metric Value
Precision 98.5%
Recall 92.4%
We evaluate following metrics to measure the quality of the output:
Intersection over Union – This is the standard metric measuring the overlap quality against the labels
Shape distance – With this metric we measure the polygon outline similarity
Dominant angle rotation error – This measures the polygon rotation deviation
Building metrics
On our evaluation set contains ~15k building. The metrics on the set are:
IoU Shape distance Rotation error [deg]
0.86 0.4 2.5
False positive ratio in the corpus
We estimate <1% false positive ratio in 1000 randomly sampled buildings from the entire output corpus.
What is the coordinate reference system?
EPSG: 4326
CREATION DETAILS
The building extraction is done in two stages:
1.Semantic Segmentation – Recognizing building pixels on the aerial image using DNNs
2.Polygonization – Converting building pixel blobs into polygons
FIRST STAGE - SEMANTIC SEGMENTATION
DNN architecture
The network foundation is ResNet34 which can be found here. In order to produce pixel prediction output, we have appended RefineNet upsampling layers described in this paper. The model is fully-convolutional, meaning that the model can be applied on an image of any size (constrained by GPU memory, 4096x4096 in our case).
Training details
The training set consists of 5 million labeled images. Majority of the satellite images cover diverse residential areas in US. For the sake of good set representation, we have enriched the set with samples from various areas covering mountains, glaciers, forests, deserts, beaches, coasts, etc. Images in the set are of 256x256 pixel size with 1 ft/pixel resolution. The training is done with CNTK toolkit using 32 GPUs.
Metrics
These are the intermediate stage metrics we use to track DNN model improvements and they are pixel based. The pixel error on the evaluation set is 1.15%. Pixel recall/precision = 94.5%/94.5%
SECOND STAGE - POLYGONIZATION
Method description
We developed a method that approximates the prediction pixels into polygons making decisions based on the whole prediction feature space. This is very different from standard approaches, e.g. Douglas-Peucker algorithm, which are greedy in nature. The method tries to impose some of a priory building properties, which are, at the moment, manually defined and automatically tuned. Some of these a priori properties are:
1.The building edge must be of at least some length, both relative and absolute, e.g. 3 meters
2.Consecutive edge angles are likely to be 90 degrees
3.Consecutive angles cannot be very sharp, smaller by some auto-tuned threshold, e.g. 30 degrees
4.Building angles likely have very few dominant angles, meaning all building edges are forming angle of (dominant angle ± nπ/2)
In near future, we will be looking to deduce this automatically from existing building information.
Metrics
Building matching metrics:
METRIC VALUE
Precision 99.3% Recall 93.5% We track various metrics to measure the quality of the output:
1.Intersection over Union – This is the standard metric measuring the overlap quality against the labels
2.Shape distance – With this metric we measure the polygon outline similarity
3.Dominant angle rotation error – This measures the polygon rotation deviation
On our evaluation set contains ~15k building. The metrics on the set are:
•IoU is 0.85, Shape distance is 0.33, Average rotation error is 1.6 degrees
•The metrics are better or similar compared to OSM building metrics against the labels
OTHER INFORMATION
Data Vintage: The vintage of the footprints depends on the vintage of the underlying imagery. Because Bing Imagery is a composite of multiple sources it is difficult to know the exact dates for individual pieces of data.
How good are the data? Our metrics show that in the vast majority of cases the quality is at least as good as data hand digitized buildings in OpenStreetMap. It is not perfect, particularly in dense urban areas but it is still awesome.
What is the coordinate reference system? EPSG: 4326
Will Microsoft be open sourcing the models? Yes. We are working through the internal process to open source the segmentation models and polyonization algorithms.
Will there be more data coming for other geographies? Maybe. This is a work in progress.
Why are the data being released? Microsoft has a continued interest in supporting a thriving OpenStreetMap ecosystem.
Should we import the data in to OpenStreetMap? Maybe. Never overwrite the hard work of other contributors or blindly import data in to OSM without first checking the local quality. While our metrics show that this data meets or exeeds the quality of hand drawn building footprints, the Data does vary in quality from place to place, between rural and urban, mountains and plains, and so on. Inspect quality locally and discuss an import plan with the community. Always follow the OSM import community guidelines.
State Number of Buildings Unzipped MB
Mississippi 1,507,496 389 MB
CONTRIBUTING:
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
LEGAL NOTICES
Microsoft and any contributors grant you a license to the Microsoft documentation in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.
Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.
Privacy information can be found at https://privacy.microsoft.com/en-us/
Microsoft and any contributors reserve all others rights, whether under their respective copyrights, patents, or trademarks, whether by implication, estoppel or otherwise.
<DIV STYLE="text-align:Left;"><DIV><DIV><TABLE><TBODY><TR><TD><P><SPAN /></P><P><SPAN><SPAN>This data is licensed by Microsoft under the </SPAN></SPAN><A href="https://opendatacommons.org/licenses/odbl/"><SPAN>Open Data Commons Open Database License ODbL)</SPAN></A></P><P><SPAN /><SPAN /></P><P><SPAN /><SPAN /></P><P><SPAN><SPAN>The MIT License (MIT) Copyright (c) Microsoft Corporation</SPAN></SPAN></P></TD></TR><TR><TD><P /></TD></TR><TR><TD><P><SPAN><SPAN>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>associated documentation files (the "Software"), to deal in the Software without restriction, </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>subject to the following conditions:</SPAN></SPAN></P></TD></TR><TR><TD><P /></TD></TR><TR><TD><P><SPAN><SPAN>The above copyright notice and this permission notice shall be included in all copies or substantial </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>portions of the Software.</SPAN></SPAN></P></TD></TR><TR><TD><P /></TD></TR><TR><TD><P><SPAN><SPAN>THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE </SPAN></SPAN></P></TD></TR><TR><TD><P><SPAN><SPAN>SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</SPAN></SPAN></P></TD></TR></TBODY></TABLE><P /><P><SPAN /></P><P><SPAN /></P></DIV></DIV></DIV>
Microsoft Windows Server 2016 Technical Preview Version 10.0 (Build 17763) ; Esri ArcGIS 13.1.2.41833
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