{"_id":"5b27cb1e4799c70003f3652a","project":"55faeacad0e22017005b8265","version":{"_id":"55faeacad0e22017005b8268","project":"55faeacad0e22017005b8265","__v":36,"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","5a96f89c89442e002041144b"],"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":9,"slug":"algorithm-guide","title":"Algorithms"},"user":"5a904b636bddb90012a75607","__v":0,"parentDoc":null,"updates":[],"next":{"pages":[],"description":""},"createdAt":"2018-06-18T15:09:18.989Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":4,"body":"## Table of Contents\n\n  Section  |  Description  \n--------|:----------\n[Imagery Examples](#imagery-examples)|Before and after examples.\n[Quickstart](#section-quickstart)|Get started with a Python-based quickstart tutorial.\n[Inputs](#inputs)|Required and optional task inputs.\n[Outputs](#outputs)|Task outputs and example contents.\n[Known Issues](#issues)|Issues users should be aware of.\n[Contact](#contact)|Contact information.  \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/d061747-dg_builtup_before_web.jpg\",\n        \"dg_builtup_before_web.jpg\",\n        566,\n        400,\n        \"#5a4e48\"\n      ],\n      \"caption\": \"WorldView 2 Image before identifying built-up extents\"\n    }\n  ]\n}\n[/block]\n\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/8dec0cb-dg_builtup_after_web.jpg\",\n        \"dg_builtup_after_web.jpg\",\n        566,\n        400,\n        \"#14e725\"\n      ],\n      \"caption\": \"WorldView 2 Image before identifying built-up extents in green\"\n    }\n  ]\n}\n[/block]\n### Quickstart\nThis is a workflow example for basic processing.\n\nPython\n```python\nfrom os.path import join, split\nfrom gbdxtools import Interface\ngbdx = Interface()\n\ntasks = []\naop_output_location = 'Digital_Globe/builtup/aop'\nbuiltup_output_location = 'Digital_Globe/builtup/task'\ncat_id = '1030010078C51A00'\n\n# Auto ordering task parameters\norder = gbdx.Task(\"Auto_Ordering\")\norder.inputs.cat_id = cat_id \norder.impersonation_allowed = True\norder.persist = True\norder.timeout = 36000\ntasks += [order]\n\n# AOP task parameters\naop = gbdx.Task(\"AOP_Strip_Processor\")\naop.inputs.data = order.outputs.s3_location.value\naop.inputs.enable_pansharpen = False\naop.inputs.enable_dra = False\naop.inputs.bands = 'MS'\naop.timeout = 36000\ntasks += [aop]\n\n# Get acquisition ID\nacq_id = split(gbdx.catalog.get_data_location(cat_id))[-1][:15]\n\n# Builtup Extent parameters \nbuiltup = gbdx.Task('protogenV2PANTEX10')\nbuiltup.inputs.raster = join('s3://gbd-customer-data', gbdx.s3.info['prefix'], aop_output_location, acq_id)\ntasks += [builtup]\n\n# Set up workflow save data\nworkflow = gbdx.Workflow(tasks)\nworkflow.savedata(aop.outputs.data, location=aop_output_location)\nworkflow.savedata(builtup.outputs.data, location=builtup_output_location)\n\n# Execute workflow\nworkflow.execute()\n```\n\n### Inputs\n\nThe following table lists all protogenV2PANTEX10 inputs.\nMandatory (optional) settings are listed as Required = True (Required = False).\n\n  Name  |  Required  |  Default  |  Valid Values  |  Description  \n--------|:----------:|-----------|----------------|---------------\nraster|True|N/A|input|Name of the geocoded, AComped image file that will be processed.\n\n\n### Outputs\n\nThe following table lists all protogenV2PANTEX10 outputs.\nMandatory (optional) settings are listed as Required = True (Required = False).\n\n  Name  |  Required  |  Default  |  Valid Values  |  Description\n--------|:----------:|-----------|----------------|---------------\ndata|True|N/A|output|The output directory of text file\n\n\n**Output structure**\n\nYour processed imagery will be written as binary .TIF image type UINT8x1 and placed in the specified S3 Customer Location   \n\n\n### Issues\n\n* Highly granular rock-covered areas might be interpreted as built-up areas.\n* Ceramic roofs are not considered.\n\n\n\n### Contact\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\n**GBDX Registered Name:**  protogenV2PANTEX10    \n**Provider:** GBDX    \n**Inputs:** This task requires an atmospherically compensated WorldView-2 or WorldView-3 multispectral image    \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:** This task requires an atmospherically compensated WorldView-2 or WorldView-3 multispectral image **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](#section-quickstart)|Get started with a Python-based quickstart tutorial. [Inputs](#inputs)|Required and optional task inputs. [Outputs](#outputs)|Task outputs and example contents. [Known Issues](#issues)|Issues users should be aware of. [Contact](#contact)|Contact information. ## <a name="Imagery Examples"></a>Imagery Example [block:image] { "images": [ { "image": [ "https://files.readme.io/d061747-dg_builtup_before_web.jpg", "dg_builtup_before_web.jpg", 566, 400, "#5a4e48" ], "caption": "WorldView 2 Image before identifying built-up extents" } ] } [/block] [block:image] { "images": [ { "image": [ "https://files.readme.io/8dec0cb-dg_builtup_after_web.jpg", "dg_builtup_after_web.jpg", 566, 400, "#14e725" ], "caption": "WorldView 2 Image before identifying built-up extents in green" } ] } [/block] ### Quickstart This is a workflow example for basic processing. Python ```python from os.path import join, split from gbdxtools import Interface gbdx = Interface() tasks = [] aop_output_location = 'Digital_Globe/builtup/aop' builtup_output_location = 'Digital_Globe/builtup/task' cat_id = '1030010078C51A00' # Auto ordering task parameters order = gbdx.Task("Auto_Ordering") order.inputs.cat_id = cat_id order.impersonation_allowed = True order.persist = True order.timeout = 36000 tasks += [order] # AOP task parameters aop = gbdx.Task("AOP_Strip_Processor") aop.inputs.data = order.outputs.s3_location.value aop.inputs.enable_pansharpen = False aop.inputs.enable_dra = False aop.inputs.bands = 'MS' aop.timeout = 36000 tasks += [aop] # Get acquisition ID acq_id = split(gbdx.catalog.get_data_location(cat_id))[-1][:15] # Builtup Extent parameters builtup = gbdx.Task('protogenV2PANTEX10') builtup.inputs.raster = join('s3://gbd-customer-data', gbdx.s3.info['prefix'], aop_output_location, acq_id) tasks += [builtup] # Set up workflow save data workflow = gbdx.Workflow(tasks) workflow.savedata(aop.outputs.data, location=aop_output_location) workflow.savedata(builtup.outputs.data, location=builtup_output_location) # Execute workflow workflow.execute() ``` ### Inputs The following table lists all protogenV2PANTEX10 inputs. Mandatory (optional) settings are listed as Required = True (Required = False). Name | Required | Default | Valid Values | Description --------|:----------:|-----------|----------------|--------------- raster|True|N/A|input|Name of the geocoded, AComped image file that will be processed. ### Outputs The following table lists all protogenV2PANTEX10 outputs. Mandatory (optional) settings are listed as Required = True (Required = False). Name | Required | Default | Valid Values | Description --------|:----------:|-----------|----------------|--------------- data|True|N/A|output|The output directory of text file **Output structure** Your processed imagery will be written as binary .TIF image type UINT8x1 and placed in the specified S3 Customer Location ### Issues * Highly granular rock-covered areas might be interpreted as built-up areas. * Ceramic roofs are not considered. ### Contact If you have any questions or issues with this task, please contact [gbdx-support@digitalglobe.com](mailto:gbdx-support@digitalglobe.com).