{"_id":"574583fc5528582000dfb292","parentDoc":null,"project":"55faeacad0e22017005b8265","user":"56267741db1eda0d001c3dbb","version":{"_id":"55faeacad0e22017005b8268","project":"55faeacad0e22017005b8265","__v":32,"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"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"v1","version_clean":"1.0.0","version":"1"},"__v":4,"category":{"_id":"56e1ccc4e416450e00b9e48c","project":"55faeacad0e22017005b8265","version":"55faeacad0e22017005b8268","pages":["56e1cdd892bf640e00b5564b","56e1cdfde416450e00b9e490","56e1ce4892bf640e00b5564e","56e1ce81e416450e00b9e494","56e1ceebe416450e00b9e497","56e1cf39cd6a8d0e00d12176","56e30b4b51857d0e008e77a3","56e30bd26e602e0e00700b16","56e30dd0d46bc30e007bb986","56e30e3d872bb20e0051ba39","56e30f91cb6ef20e0084f23c"],"__v":11,"sync":{"url":"","isSync":false},"reference":false,"createdAt":"2016-03-10T19:36:36.026Z","from_sync":false,"order":17,"slug":"aggregations-facets-vector-services-guide","title":"Aggregations & Facets Vector Services Guide"},"updates":[],"next":{"pages":[],"description":""},"createdAt":"2016-05-25T10:52:44.411Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":5,"body":"Something to Note: For ObjectDetection, the item_type is really the highest scoring object class. Scores are listed in the attribute fields.\n\n<h2>Attribute Fields</h2>\n\nThe following table is a list of ObjectDetection attributes that users can query on, as well as the type and a brief description of each attribute.\n[block:parameters]\n{\n  \"data\": {\n    \"h-0\": \"Properties\",\n    \"h-1\": \"Type\",\n    \"h-2\": \"Description\",\n    \"3-0\": \"best_Background-AOI1_dbl\",\n    \"5-0\": \"best_Helicopter_dbl\",\n    \"7-0\": \"best_Urban_dbl\",\n    \"10-0\": \"item_date\",\n    \"11-0\": \"models\",\n    \"13-0\": \"model_Background-AOI1_dbl\",\n    \"14-0\": \"model_Fighter_dbl\",\n    \"15-0\": \"model_Helicopter_dbl\",\n    \"16-0\": \"model_Trees_dbl\",\n    \"17-0\": \"model_Urban_dbl\",\n    \"18-0\": \"sat_id\",\n    \"3-2\": \"The score for the Background model for the AOI.\\n*Note: The 1 is changeable based on the AOI being referenced and the number of AOIs in the project.*\",\n    \"5-2\": \"The score for Helicopter object across all models, derived during the workflow that produced the vector.\\n*Note: This is generally identical to the model_Helicopter_dbl score.*\",\n    \"7-2\": \"The score for Urban object across all models, derived during the workflow that produced the vector.\\n*Note: This is generally identical to the model_Urban_dbl score.*\",\n    \"10-2\": \"datetime that the acquisition image referenced by the cat_id field enters the catalog listing; format: strict_date_optional_time epoch_millis\",\n    \"11-2\": \"The model(s) used in the algorithm that generated the vector.\",\n    \"13-2\": \"The score for the Background model for the AOI.\\n*Note: The 1 is changeable based on the AOI being referenced and the number of AOIs in the project.*\",\n    \"14-2\": \"The score for the Fighter model derived during the workflow that produced the vector.\\n*Note: This is generally identical to the best_Fighter_dbl score.*\",\n    \"15-2\": \"The score for the Helicopter model derived during the workflow that produced the vector.\\n*Note: This is generally identical to the best_Helicopter_dbl score.*\",\n    \"16-2\": \"The score for the Trees model derived during the workflow that produced the vector.\\n*Note: This is generally identical to the best_Trees_dbl score.*\",\n    \"17-2\": \"The score for the Urban model derived during the workflow that produced the vector.\\n*Note: This is generally identical to the best_Urban_dbl score.*\",\n    \"18-2\": \"The satellite used to capture the imagery.\\nSatellite options include: WV01, WV02, WV03, GE01, QB02\",\n    \"10-1\": \"Date\",\n    \"15-1\": \"Double\",\n    \"3-1\": \"Double\",\n    \"5-1\": \"Double\",\n    \"7-1\": \"Double\",\n    \"11-1\": \"String\",\n    \"13-1\": \"Double\",\n    \"14-1\": \"Double\",\n    \"16-1\": \"Double\",\n    \"17-1\": \"Double\",\n    \"18-1\": \"String\",\n    \"4-0\": \"best_Fighter_dbl\",\n    \"8-0\": \"cat_id\",\n    \"6-0\": \"best_Trees_dbl\",\n    \"8-1\": \"String\",\n    \"4-1\": \"Double\",\n    \"6-1\": \"Double\",\n    \"2-0\": \"best_Airliner_dbl\",\n    \"2-1\": \"Double\",\n    \"12-0\": \"model_Airliner_dbl\",\n    \"12-1\": \"Double\",\n    \"8-2\": \"The id of the catalog acquisition image used in processing to generate the vector.\",\n    \"9-0\": \"cat_id_raw\",\n    \"9-1\": \"String\",\n    \"9-2\": \"The raw id value of the catalog acquisition image used in processing to generate the vector.\",\n    \"0-0\": \"acquisition_date\",\n    \"0-1\": \"Date\",\n    \"0-2\": \"datetime that the acquisition image referenced by the cat_id field enters the catalog listing; format: strict_date_optional_time epoch_millis\",\n    \"1-0\": \"acquisition_id_raw\",\n    \"1-1\": \"String\",\n    \"1-2\": \"The raw id value of the catalog acquisition image used in processing to generate the vector.\",\n    \"12-2\": \"The score for the Airliner model derived during the workflow that produced the vector.\\n*Note: This is generally identical to the best_Airliner_dbl score.*\",\n    \"2-2\": \"The score for Airliner object across all models, derived during the workflow that produced the vector.\\n*Note: This is generally identical to the model_Airliner_dbl score.*\",\n    \"4-2\": \"The score for Fighter object across all models, derived during the workflow that produced the vector.\\n*Note: This is generally identical to the model_Fighter_dbl score.*\",\n    \"6-2\": \"The score for Trees object across all models, derived during the workflow that produced the vector.\\n*Note: This is generally identical to the model_Trees_dbl score.*\"\n  },\n  \"cols\": 3,\n  \"rows\": 19\n}\n[/block]\nThe following table is a list of ObjectDetection ingest_attributes that users can query on, as well as the type and a brief description of each attribute.\n[block:parameters]\n{\n  \"data\": {\n    \"h-0\": \"Properties\",\n    \"h-1\": \"Type\",\n    \"h-2\": \"Description\",\n    \"1-0\": \"_rest_user\",\n    \"3-0\": \"_rest_url\",\n    \"1-1\": \"String\",\n    \"3-1\": \"String\",\n    \"1-2\": \"The name of the \\\"user\\\" that the vector was ingested under.\",\n    \"3-2\": \"The url that the vector was ingested through.\",\n    \"0-0\": \"recipe_id_raw\",\n    \"0-1\": \"String\",\n    \"0-2\": \"The id of the recipe that has been executed to produce the vector.\",\n    \"2-0\": \"project_id_raw\",\n    \"2-1\": \"String\",\n    \"4-0\": \"run_id_raw\",\n    \"4-1\": \"String\",\n    \"2-2\": \"The id of the project that the vector is associated with upon vector creation.\",\n    \"4-2\": \"The unique id used to group vectors together that are the result of the same workflow/recipe run.\"\n  },\n  \"cols\": 3,\n  \"rows\": 5\n}\n[/block]","excerpt":"Attribute fields specific to ObjectDetection image processing.","slug":"objectdetection-attributes","type":"basic","title":"ObjectDetection Attributes"}

ObjectDetection Attributes

Attribute fields specific to ObjectDetection image processing.

Something to Note: For ObjectDetection, the item_type is really the highest scoring object class. Scores are listed in the attribute fields. <h2>Attribute Fields</h2> The following table is a list of ObjectDetection attributes that users can query on, as well as the type and a brief description of each attribute. [block:parameters] { "data": { "h-0": "Properties", "h-1": "Type", "h-2": "Description", "3-0": "best_Background-AOI1_dbl", "5-0": "best_Helicopter_dbl", "7-0": "best_Urban_dbl", "10-0": "item_date", "11-0": "models", "13-0": "model_Background-AOI1_dbl", "14-0": "model_Fighter_dbl", "15-0": "model_Helicopter_dbl", "16-0": "model_Trees_dbl", "17-0": "model_Urban_dbl", "18-0": "sat_id", "3-2": "The score for the Background model for the AOI.\n*Note: The 1 is changeable based on the AOI being referenced and the number of AOIs in the project.*", "5-2": "The score for Helicopter object across all models, derived during the workflow that produced the vector.\n*Note: This is generally identical to the model_Helicopter_dbl score.*", "7-2": "The score for Urban object across all models, derived during the workflow that produced the vector.\n*Note: This is generally identical to the model_Urban_dbl score.*", "10-2": "datetime that the acquisition image referenced by the cat_id field enters the catalog listing; format: strict_date_optional_time epoch_millis", "11-2": "The model(s) used in the algorithm that generated the vector.", "13-2": "The score for the Background model for the AOI.\n*Note: The 1 is changeable based on the AOI being referenced and the number of AOIs in the project.*", "14-2": "The score for the Fighter model derived during the workflow that produced the vector.\n*Note: This is generally identical to the best_Fighter_dbl score.*", "15-2": "The score for the Helicopter model derived during the workflow that produced the vector.\n*Note: This is generally identical to the best_Helicopter_dbl score.*", "16-2": "The score for the Trees model derived during the workflow that produced the vector.\n*Note: This is generally identical to the best_Trees_dbl score.*", "17-2": "The score for the Urban model derived during the workflow that produced the vector.\n*Note: This is generally identical to the best_Urban_dbl score.*", "18-2": "The satellite used to capture the imagery.\nSatellite options include: WV01, WV02, WV03, GE01, QB02", "10-1": "Date", "15-1": "Double", "3-1": "Double", "5-1": "Double", "7-1": "Double", "11-1": "String", "13-1": "Double", "14-1": "Double", "16-1": "Double", "17-1": "Double", "18-1": "String", "4-0": "best_Fighter_dbl", "8-0": "cat_id", "6-0": "best_Trees_dbl", "8-1": "String", "4-1": "Double", "6-1": "Double", "2-0": "best_Airliner_dbl", "2-1": "Double", "12-0": "model_Airliner_dbl", "12-1": "Double", "8-2": "The id of the catalog acquisition image used in processing to generate the vector.", "9-0": "cat_id_raw", "9-1": "String", "9-2": "The raw id value of the catalog acquisition image used in processing to generate the vector.", "0-0": "acquisition_date", "0-1": "Date", "0-2": "datetime that the acquisition image referenced by the cat_id field enters the catalog listing; format: strict_date_optional_time epoch_millis", "1-0": "acquisition_id_raw", "1-1": "String", "1-2": "The raw id value of the catalog acquisition image used in processing to generate the vector.", "12-2": "The score for the Airliner model derived during the workflow that produced the vector.\n*Note: This is generally identical to the best_Airliner_dbl score.*", "2-2": "The score for Airliner object across all models, derived during the workflow that produced the vector.\n*Note: This is generally identical to the model_Airliner_dbl score.*", "4-2": "The score for Fighter object across all models, derived during the workflow that produced the vector.\n*Note: This is generally identical to the model_Fighter_dbl score.*", "6-2": "The score for Trees object across all models, derived during the workflow that produced the vector.\n*Note: This is generally identical to the model_Trees_dbl score.*" }, "cols": 3, "rows": 19 } [/block] The following table is a list of ObjectDetection ingest_attributes that users can query on, as well as the type and a brief description of each attribute. [block:parameters] { "data": { "h-0": "Properties", "h-1": "Type", "h-2": "Description", "1-0": "_rest_user", "3-0": "_rest_url", "1-1": "String", "3-1": "String", "1-2": "The name of the \"user\" that the vector was ingested under.", "3-2": "The url that the vector was ingested through.", "0-0": "recipe_id_raw", "0-1": "String", "0-2": "The id of the recipe that has been executed to produce the vector.", "2-0": "project_id_raw", "2-1": "String", "4-0": "run_id_raw", "4-1": "String", "2-2": "The id of the project that the vector is associated with upon vector creation.", "4-2": "The unique id used to group vectors together that are the result of the same workflow/recipe run." }, "cols": 3, "rows": 5 } [/block]