{"_id":"57dc3adf702ffe0e00157c63","project":"55faeacad0e22017005b8265","user":"55fae9d4825d5f19001fa379","__v":1,"parentDoc":null,"version":{"_id":"55faeacad0e22017005b8268","project":"55faeacad0e22017005b8265","__v":35,"createdAt":"2015-09-17T16:31:06.800Z","releaseDate":"2015-09-17T16:31:06.800Z","categories":["55faeacbd0e22017005b8269","55faf550764f50210095078e","55faf5b5626c341700fd9e96","55faf8a7825d5f19001fa386","560052f91503430d007cc88f","560054f73aa0520d00da0b1a","56005aaf6932a00d00ba7c62","56005c273aa0520d00da0b3f","5601ae7681a9670d006d164d","5601ae926811d00d00ceb487","5601aeb064866b1900f4768d","5601aee850ee460d0002224c","5601afa02499c119000faf19","5601afd381a9670d006d1652","561d4c78281aec0d00eb27b6","561d588d8ca8b90d00210219","563a5f934cc3621900ac278c","5665c5763889610d0008a29e","566710a36819320d000c2e93","56ddf6df8a5ae10e008e3926","56e1c96b2506700e00de6e83","56e1ccc4e416450e00b9e48c","56e1ccdfe63f910e00e59870","56e1cd10bc46be0e002af26a","56e1cd21e416450e00b9e48e","56e3139a51857d0e008e77be","573b4f62ef164e2900a2b881","57c9d1335fd8ca0e006308ed","57e2bd9d1e7b7220000d7fa5","57f2b992ac30911900c7c2b6","58adb5c275df0f1b001ed59b","58c81b5c6dc7140f003c3c46","595412446ed4d9001b3e7b37","59e76ce41938310028037295","5a009de510890d001c2aabfe"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"v1","version_clean":"1.0.0","version":"1"},"category":{"_id":"573b4f62ef164e2900a2b881","__v":0,"project":"55faeacad0e22017005b8265","version":"55faeacad0e22017005b8268","sync":{"url":"","isSync":false},"reference":false,"createdAt":"2016-05-17T17:05:38.443Z","from_sync":false,"order":8,"slug":"algorithm-guide","title":"Algorithms"},"updates":[],"next":{"pages":[],"description":""},"createdAt":"2016-09-16T18:33:03.910Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"settings":"","results":{"codes":[]},"auth":"required","params":[],"url":""},"isReference":false,"order":6,"body":"## Table of Contents\n\nSection | Description\n--- | ---\n[Imagery Examples](#Imagery Examples) | Before and after examples\n[Quickstart](#Quickstart) | Get started with a Python-based quickstart tutorial\n[Task Runtime](#Task Runtime) | Benchmark runtimes for the algorithm\n[Input Options](#Input Options) | Required and optional task inputs\n[Outputs](#Outputs) | Task outputs and example contents\n[Advanced Options](#Advanced Options) | Additional information for advanced users\n[Known Issues](#Known Issues) | Issues users should be aware of\n\n\n\n## <a name=\"Imagery Examples\"></a>Imagery Example\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/9be5def-PANTEX800x320.png\",\n        \"PANTEX800x320.png\",\n        800,\n        320,\n        \"#aba5a4\"\n      ],\n      \"caption\": \"This image shows the image before and after the Built-up Extent layer is applied.\"\n    }\n  ]\n}\n[/block]\n\n## <a name=\"Quickstart\"></a>Quickstart Tutorial\n\nThis script gives the example of a built-up extent layer with a single tif file as input. \n[block:code]\n{\n  \"codes\": [\n    {\n      \"code\": \"   from gbdxtools import Interface\\n    gbdx = Interface()\\n\\n    #Edit the following path to reflect a specific path to an image\\n    raster = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\\n    prototask = gbdx.Task(\\\"protogenV2PANTEX10\\\", raster=raster)\\n\\n    workflow = gbdx.Workflow([ prototask ])  \\n    #Edit the following line(s) to reflect specific folder(s) for the output file (example location provided)\\n    workflow.savedata(prototask.outputs.data, location=\\\"protogen/BuiltUpExtent\\\")\\n    workflow.execute()\\n\\n    print workflow.id\\n    print workflow.status\",\n      \"language\": \"python\"\n    }\n  ]\n}\n[/block]\n\n## <a name=\"Task Runtime\"></a>Task Runtime\nThese are the average runtimes for this algorithm. All benchmark tests were run using a standard set of images, based on our most common customer scenarios. Runtime benchmarks apply to the specific algorithm, and don’t represent the runtime of a complete workflow.\n\n  Sensor Name  |  Total Pixels  |  Total Area (k2)  |  Time(secs)  |  Time/Area k2\n--------|:----------:|-----------|----------------|---------------\nWV02|35,872,942|329.87|118.36 |0.36\nWV03|35,371,971|196.27| 161.39|0.82 \n\n## <a name=\"Input Options\"></a>Input Options\nWorldView 2 or WorldView 3 multi-spectral imagery (8-band optical and VNIR data sets) that has been atmospherically compensated by theAdvanced Image Preprocessor.  Supported formats are .TIF\n\nName                     |       Default         |        Valid Values             |   Description\n-------------------------|:---------------------:|---------------------------------|-----------------\nraster                   |          N/A          | S3 URL   .TIF only              | S3 location of input .tif file processed through AOP_Strip_Processor.\n\n## <a name=\"Outputs\"></a>Outputs\nThe following table lists the Built-up Extent task outputs.\n\nName | Required |   Description\n-----|:--------:|-----------------\ndata |     Y    | This will explain the output file location and provide the output in .TIF format.\nlog  |     N    | S3 location where logs are stored.\n\nYour processed imagery will be written as binary .TIF image type UINT8x1 and placed in the specified S3 Customer Location (e.g.  s3://gbd-customer-data/unique customer id/named directory/). \n\n## <a name=\"Advanced Options\"></a>Advanced Options\nNo additional optional settings for this task exist\n\n## <a name=\"Known Issues\"></a>Known Issues\nHighly granular rock-covered areas might be interpreted as built-up areas.\n\nLimitations: Ceramic roofs are not considered.\n\n#### Contact Us   \nIf you have any questions or issues with this task, please contact [**gbdx-support:::at:::digitalglobe.com** ](mailto:gbdx-support@digitalglobe.com).","excerpt":"This task uses 8-band imagery to identify human built-up areas. It creates a mask layer that supplements Automated Land Cover Classification. This unsupervised process creates a binary image where intensity 255 shows that a pixel is likely part of a built-up area, and intensity 0 shows that a pixel is likely not part of a built-up area. \n\n**GBDX Registered Name**: protogenV2PANTEX10\n**Provider**: GBDX\n**Inputs**: .TIF, .TIL,  .HDR\n**Outputs**: TIF image type UINT8x1\n**Compatible bands & sensors**: WorldView 2 or WorldView 3 multi-spectral imagery (8-band optical and VNIR data sets) that has been atmospherically compensated by the AOP processor","slug":"built-up-extent","type":"basic","title":"Built-up Extent"}

Built-up Extent

This task uses 8-band imagery to identify human built-up areas. It creates a mask layer that supplements Automated Land Cover Classification. This unsupervised process creates a binary image where intensity 255 shows that a pixel is likely part of a built-up area, and intensity 0 shows that a pixel is likely not part of a built-up area. **GBDX Registered Name**: protogenV2PANTEX10 **Provider**: GBDX **Inputs**: .TIF, .TIL, .HDR **Outputs**: TIF image type UINT8x1 **Compatible bands & sensors**: WorldView 2 or WorldView 3 multi-spectral imagery (8-band optical and VNIR data sets) that has been atmospherically compensated by the AOP processor

## Table of Contents Section | Description --- | --- [Imagery Examples](#Imagery Examples) | Before and after examples [Quickstart](#Quickstart) | Get started with a Python-based quickstart tutorial [Task Runtime](#Task Runtime) | Benchmark runtimes for the algorithm [Input Options](#Input Options) | Required and optional task inputs [Outputs](#Outputs) | Task outputs and example contents [Advanced Options](#Advanced Options) | Additional information for advanced users [Known Issues](#Known Issues) | Issues users should be aware of ## <a name="Imagery Examples"></a>Imagery Example [block:image] { "images": [ { "image": [ "https://files.readme.io/9be5def-PANTEX800x320.png", "PANTEX800x320.png", 800, 320, "#aba5a4" ], "caption": "This image shows the image before and after the Built-up Extent layer is applied." } ] } [/block] ## <a name="Quickstart"></a>Quickstart Tutorial This script gives the example of a built-up extent layer with a single tif file as input. [block:code] { "codes": [ { "code": " from gbdxtools import Interface\n gbdx = Interface()\n\n #Edit the following path to reflect a specific path to an image\n raster = 's3://gbd-customer-data/CustomerAccount#/PathToImage/'\n prototask = gbdx.Task(\"protogenV2PANTEX10\", raster=raster)\n\n workflow = gbdx.Workflow([ prototask ]) \n #Edit the following line(s) to reflect specific folder(s) for the output file (example location provided)\n workflow.savedata(prototask.outputs.data, location=\"protogen/BuiltUpExtent\")\n workflow.execute()\n\n print workflow.id\n print workflow.status", "language": "python" } ] } [/block] ## <a name="Task Runtime"></a>Task Runtime These are the average runtimes for this algorithm. All benchmark tests were run using a standard set of images, based on our most common customer scenarios. Runtime benchmarks apply to the specific algorithm, and don’t represent the runtime of a complete workflow. Sensor Name | Total Pixels | Total Area (k2) | Time(secs) | Time/Area k2 --------|:----------:|-----------|----------------|--------------- WV02|35,872,942|329.87|118.36 |0.36 WV03|35,371,971|196.27| 161.39|0.82 ## <a name="Input Options"></a>Input Options WorldView 2 or WorldView 3 multi-spectral imagery (8-band optical and VNIR data sets) that has been atmospherically compensated by theAdvanced Image Preprocessor. Supported formats are .TIF Name | Default | Valid Values | Description -------------------------|:---------------------:|---------------------------------|----------------- raster | N/A | S3 URL .TIF only | S3 location of input .tif file processed through AOP_Strip_Processor. ## <a name="Outputs"></a>Outputs The following table lists the Built-up Extent task outputs. Name | Required | Description -----|:--------:|----------------- data | Y | This will explain the output file location and provide the output in .TIF format. log | N | S3 location where logs are stored. Your processed imagery will be written as binary .TIF image type UINT8x1 and placed in the specified S3 Customer Location (e.g. s3://gbd-customer-data/unique customer id/named directory/). ## <a name="Advanced Options"></a>Advanced Options No additional optional settings for this task exist ## <a name="Known Issues"></a>Known Issues Highly granular rock-covered areas might be interpreted as built-up areas. Limitations: Ceramic roofs are not considered. #### Contact Us If you have any questions or issues with this task, please contact [**gbdx-support@digitalglobe.com** ](mailto:gbdx-support@digitalglobe.com).