Last active
October 9, 2019 17:48
-
-
Save up42-epicycles/59882a5ed08396c13321f0217db0e914 to your computer and use it in GitHub Desktop.
UP42 API: Update workflow response.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "error": null, | |
| "data": [ | |
| { | |
| "id": "30ad2045-dce2-4d40-a3bf-4a9f2336c2ee", | |
| "displayId": "30ad2045", | |
| "createdAt": "2019-10-09T17:46:20.027Z", | |
| "updatedAt": "2019-10-09T17:46:20.027Z", | |
| "name": "First task SPOT 6/7 AOI clipped data block", | |
| "parentsIds": [], | |
| "blockName": "oneatlas-spot-aoiclipped", | |
| "blockVersionTag": "1.3.1", | |
| "block": { | |
| "id": "0f15e07f-efcc-4598-939b-18aade349c57", | |
| "createdAt": "2019-02-20T16:21:00.984Z", | |
| "updatedAt": "2019-10-01T13:06:02.123Z", | |
| "name": "oneatlas-spot-aoiclipped", | |
| "displayName": "SPOT 6/7 Streaming", | |
| "description": "This block provides AOI-clipped versions of the imagery from the SPOT NAOMI sensor. SPOT 6/7 are high-resolution twin satellites offering 1.5m resolution products. Currently supported are pan-sharpened RGB and the panchromatic band. The block is an easy-to-use starting point for analysis workflows needing high-resolution input data.", | |
| "containerUrl": "registry.up42.dev/marketplace/oneatlas-spot-aoiclipped:MfetDOXePRJLbcSSAWtxQIMxdNsphnEnKvZoLIDx", | |
| "inputCapabilities": [], | |
| "outputCapabilities": [ | |
| { | |
| "name": "up42.data.aoiclipped" | |
| } | |
| ], | |
| "provider": "OneAtlas", | |
| "tags": [ | |
| "Airbus", | |
| "SPOT", | |
| "global", | |
| "OneAtlas", | |
| "high revisit", | |
| "optical", | |
| "high resolution" | |
| ], | |
| "isPublic": true, | |
| "isPublicVersion": true, | |
| "isValid": true, | |
| "parameters": { | |
| "ids": { | |
| "type": "array", | |
| "default": null | |
| }, | |
| "bbox": { | |
| "type": "array", | |
| "default": null | |
| }, | |
| "time": { | |
| "type": "dateRange", | |
| "default": "2018-01-01T00:00:00+00:00/2019-12-31T23:59:59+00:00" | |
| }, | |
| "limit": { | |
| "type": "integer", | |
| "default": 1, | |
| "minimum": 1 | |
| }, | |
| "contains": { | |
| "type": "geometry" | |
| }, | |
| "intersects": { | |
| "type": "geometry" | |
| }, | |
| "zoom_level": { | |
| "type": "integer", | |
| "default": 17, | |
| "maximum": 17, | |
| "minimum": 9 | |
| }, | |
| "time_series": { | |
| "type": "array", | |
| "default": null | |
| }, | |
| "panchromatic_band": { | |
| "type": "boolean", | |
| "default": false | |
| } | |
| }, | |
| "type": "DATA", | |
| "isDryRunSupported": true, | |
| "version": "1.3.1", | |
| "metadata": { | |
| "overview": "SPOT 6/7 are high-resolution twin satellites offering 1.5m resolution products. They can cover large-area in single pass up to 100,000km². They are especially designed to cover nationwide areas within a season.\n\n This block provides AOI-clipped versions of the imagery from the SPOT NAOMI sensor. The output image contains all the web Mercator tiles intersecting or covering the input AOI (depending on the geometric filter used) in a resolution defined by the used zoom level (see this [link](https://wiki.openstreetmap.org/wiki/Zoom_levels) for the definition of zoom levels). \n\nThe available output bands of the SPOT block are: red, green, blue, and panchromatic (current version does not support NIR).The default behaviour is to only deliver RGB, the panchromatic band needs to be requested using a query parameter.\n\n Many analytics blocks cannot process the output of this block directly but need it to be pre-processed using the Tiling block. Please check the corresponding block descriptions. \n\n\n**Technical information**\n\n|Feature|Info|\n|:-|:-|\n|Archive date / refresh frequency | Archives available starting from 2016. Full Earth accessible each day.\n|Frequency bands | Panchromatic: 0.450-0.745 μm<br>Blue: 0.450-0.520 μm<br>Green: 0.530-0.590 μm<br>Red: 0.625-0.695 μm<br>Near Infrared: 0.760-0.890 μm |\n|Resolution|Panchromatic: 1.5m<br>Multispectral: 1.5m<br>Swath: 60km at nadir|\n|Processing level|Ortho Bundle 8 bits Display GeoTIFF (Level3)|\n|Data format| GeoTIFF|\n\n**Band information and revisit rate**\n\n| Band Category | Spatial Resolution| Revisit|\n|:---|:----:|:---|\n| Visible (3) | 1.5 m | 1 day |\n| Near-Infrared (1) | 1.5 m | 1 day|\n| Panchromatic (1) | 1.5 m | 1 day |\n\n**Use cases**\n\nFor more information about potential use cases, see the [case study gallery](https://oneatlas.airbus.com/case-study/).\n\n**More information**\n\nFor more information about this data, please see the [provider website](https://oneatlas.airbus.com/)", | |
| "termsAndConditionsUrl": "https://metadata.up42.com/OneAtlas/SPOT_AOI/r17523_9_eula-spot1-7-vuk-oct2017.pdf", | |
| "iconUrl": "https://metadata.up42.com/OneAtlas/SPOT_AOI/0_SPOT_6-7_Avatar.jpg", | |
| "pricingStrategy": { | |
| "id": "c6e24d4a-7469-4415-9781-11bb2e9ef413", | |
| "displayId": "c6e24d4a", | |
| "createdAt": "2019-10-01T13:06:02.114Z", | |
| "updatedAt": "2019-10-01T13:06:02.114Z", | |
| "type": "TILE_OUTPUT", | |
| "credits": 3 | |
| }, | |
| "blockMarketplaceSampleData": [ | |
| { | |
| "id": "1a6b2ab1-7bfe-4764-8409-581c647e72cd", | |
| "displayId": "1a6b2ab1", | |
| "createdAt": "2019-10-01T13:06:02.117Z", | |
| "updatedAt": "2019-10-01T13:06:02.117Z", | |
| "value": "https://metadata.up42.com/OneAtlas/SPOT_AOI/SPOT_6-7_Cameroon_Ngambe_Tikar.jpg", | |
| "type": "IMAGE" | |
| }, | |
| { | |
| "id": "b7378a0c-ab71-44b0-be29-cd41f0c14dda", | |
| "displayId": "b7378a0c", | |
| "createdAt": "2019-10-01T13:06:02.118Z", | |
| "updatedAt": "2019-10-01T13:06:02.118Z", | |
| "value": "https://metadata.up42.com/OneAtlas/SPOT_AOI/SPOT_6-7_Fiji.jpg", | |
| "type": "IMAGE" | |
| }, | |
| { | |
| "id": "b8d70f4f-9302-4013-b47b-384e204cf2db", | |
| "displayId": "b8d70f4f", | |
| "createdAt": "2019-10-01T13:06:02.119Z", | |
| "updatedAt": "2019-10-01T13:06:02.119Z", | |
| "value": "https://metadata.up42.com/OneAtlas/SPOT_AOI/SPOT_6-7_France_Paris.jpg", | |
| "type": "IMAGE" | |
| }, | |
| { | |
| "id": "339d39fa-ec7b-4e3c-be17-432ca21895a5", | |
| "displayId": "339d39fa", | |
| "createdAt": "2019-10-01T13:06:02.121Z", | |
| "updatedAt": "2019-10-01T13:06:02.121Z", | |
| "value": "https://metadata.up42.com/OneAtlas/SPOT_AOI/SPOT_6-7_Galapagos_Wolf_Volcano.jpg", | |
| "type": "IMAGE" | |
| }, | |
| { | |
| "id": "b2b72fef-9d1f-402c-bc5c-eeff3e178fbf", | |
| "displayId": "b2b72fef", | |
| "createdAt": "2019-10-01T13:06:02.122Z", | |
| "updatedAt": "2019-10-01T13:06:02.122Z", | |
| "value": "https://metadata.up42.com/OneAtlas/SPOT_AOI/SPOT_6-7_Namibia_Namib_Desert.jpg", | |
| "type": "IMAGE" | |
| } | |
| ] | |
| }, | |
| "machineName": "LARGE" | |
| } | |
| }, | |
| { | |
| "id": "f46d9b0a-6e20-4d78-ac8b-d8d5403b4d24", | |
| "displayId": "f46d9b0a", | |
| "createdAt": "2019-10-09T17:46:20.042Z", | |
| "updatedAt": "2019-10-09T17:46:20.042Z", | |
| "name": "land-cover-classification", | |
| "parentsIds": [ | |
| "30ad2045-dce2-4d40-a3bf-4a9f2336c2ee" | |
| ], | |
| "blockName": "land-cover-classification", | |
| "blockVersionTag": "1.0.3", | |
| "block": { | |
| "id": "3f5f4490-9e58-490f-80e0-9a464355d5ce", | |
| "createdAt": "2019-08-09T09:38:14.613Z", | |
| "updatedAt": "2019-08-12T14:53:30.964Z", | |
| "name": "land-cover-classification", | |
| "displayName": "DEMO: Land Cover Classification", | |
| "description": "This block takes an AOI clipped image and runs land cover classification on it.\nThe classification is a simple unsupervised K-means clustering in the color space of the image.\nThe number of clusters and other parameters can be specified for the classification.", | |
| "containerUrl": "registry.up42.dev/marketplace/land-cover-classification:7nyITZbx7smddUx5p8xCvdBtndkIXYNRoTV9DWZz", | |
| "inputCapabilities": [ | |
| { | |
| "name": "up42.data.aoiclipped" | |
| } | |
| ], | |
| "outputCapabilities": [ | |
| { | |
| "name": "custom.processing.land_cover" | |
| } | |
| ], | |
| "provider": "UP42", | |
| "tags": [ | |
| "imagery", | |
| "processing", | |
| "machine learning" | |
| ], | |
| "isPublic": true, | |
| "isPublicVersion": true, | |
| "isValid": true, | |
| "parameters": { | |
| "n_clusters": { | |
| "type": "number", | |
| "default": 6, | |
| "required": false, | |
| "description": "The number of clusters for the K-means clustering" | |
| }, | |
| "n_iterations": { | |
| "type": "number", | |
| "default": 10, | |
| "required": false, | |
| "description": "The number of iterations for the K-means clustering" | |
| }, | |
| "n_sieve_pixels": { | |
| "type": "number", | |
| "default": 64, | |
| "required": false, | |
| "description": "Minimum number of pixels in each patch for the classification" | |
| } | |
| }, | |
| "type": "PROCESSING", | |
| "isDryRunSupported": false, | |
| "version": "1.0.3", | |
| "metadata": { | |
| "overview": "Land Cover is the physical material at the surface of the earth that includes grass, asphalt, trees, bare ground, water along with some other types (not to be confused with land use). Land cover information plays an essential role in many fields including forest and rangeland monitoring, municipal planning, desertification studies, identifying exposed soil for erosion risk and more. \n\n This block is a case study of the ability to integrate the Tensorflow framework on the platform, unleashing the potential to create neural networks for machine-learning. The source code is published as Free Software / Open Source and can be found at https://github.com/up42/land-cover-classification-demo .\n\n| General Information | Description |\n| --------------------- |-----------------------------------------------------------------------------|\n| Block Type | classification (machine learning) |\n| Supported Input Types | all raster data |\n| Resolution | identical to the input |\n| Performance | This is a proof of concept. No quality or accuracy measures were taken. |\n", | |
| "termsAndConditionsUrl": "https://up42.com/legal/privacy-policy", | |
| "iconUrl": "https://metadata.up42.dev/UP42_Blocks/LandCover/LC1_avatar.png", | |
| "pricingStrategy": { | |
| "id": "2319e095-4db3-4da2-8181-7b1caf530071", | |
| "displayId": "2319e095", | |
| "createdAt": "2019-08-12T14:53:30.977Z", | |
| "updatedAt": "2019-08-12T14:53:30.977Z", | |
| "type": "MEGABYTE_INPUT", | |
| "credits": 1 | |
| }, | |
| "blockMarketplaceSampleData": [ | |
| { | |
| "id": "ed05eaf5-1385-4227-8bd1-70f117c47862", | |
| "displayId": "ed05eaf5", | |
| "createdAt": "2019-08-12T14:53:30.984Z", | |
| "updatedAt": "2019-08-12T14:53:30.984Z", | |
| "value": "https://metadata.up42.dev/UP42_Blocks/LandCover/LC2_orig.png", | |
| "type": "IMAGE" | |
| }, | |
| { | |
| "id": "4db438fe-7d3a-4392-998f-b7abdf63bc3e", | |
| "displayId": "4db438fe", | |
| "createdAt": "2019-08-12T14:53:30.982Z", | |
| "updatedAt": "2019-08-12T14:53:30.982Z", | |
| "value": "https://metadata.up42.dev/UP42_Blocks/LandCover/LC1_orig.png", | |
| "type": "IMAGE" | |
| }, | |
| { | |
| "id": "652122ca-4931-47c4-8561-72cad8f96cdf", | |
| "displayId": "652122ca", | |
| "createdAt": "2019-08-12T14:53:30.983Z", | |
| "updatedAt": "2019-08-12T14:53:30.983Z", | |
| "value": "https://metadata.up42.dev/UP42_Blocks/LandCover/LC1_output.png", | |
| "type": "IMAGE" | |
| }, | |
| { | |
| "id": "4f124e0e-56b7-42ab-b168-b8392afe9325", | |
| "displayId": "4f124e0e", | |
| "createdAt": "2019-08-12T14:53:30.985Z", | |
| "updatedAt": "2019-08-12T14:53:30.985Z", | |
| "value": "https://metadata.up42.dev/UP42_Blocks/LandCover/LC2_output.png", | |
| "type": "IMAGE" | |
| } | |
| ] | |
| }, | |
| "machineName": "LARGE" | |
| } | |
| } | |
| ] | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment