이란 전역의 토지 피복 지도는 Google Earth Engine Cloud 플랫폼 내에서 Sentinel 이미지를 처리하여 생성되었습니다. 이를 위해 2017년의 단일 모자이크 데이터 세트를 생성하기 위해 2,500개가 넘는 Sentinel-1 이미지와 11,000개가 넘는 Sentinel-2 이미지가 처리되었습니다. 그런 다음 13개 클래스의 많은 참조 샘플로 객체 기반 랜덤 포레스트 분류 방법을 학습시켜 이란 전역 토지 피복 지도를 생성했습니다.
대역
대역
이름
픽셀 크기
설명
classification
10미터
분류
분류 클래스 표
값
색상
설명
1
#000000
도시
2
#006eff
물
3
#41a661
습지대
4
#ff7f7f
Kalut (yardang)
5
#bee8ff
Marshland
6
#ff00c5
Salty Land
7
#ff0000
클레이
8
#00734c
숲
9
#732600
노두
10
#ffaa00
Uncovered Plain
11
#d3ffbe
모래
12
#446589
농지
13
#cccccc
Range Land
이용약관
이용약관
Arsalan Ghorbanian, Mohammad Kakooei, Meisam Amani, Sahel Mahdavi, Ali Mohammadzadeh, Mahdi Hasanlou의 'Iran Land Cover Map v1 13-class (2017)'은 크리에이티브 커먼즈 저작자표시 4.0 국제 라이선스 (CC BY 4.0)에 따라 이용할 수 있습니다.
인용
인용:
Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., &
Hasanlou, M. (2020). Google Earth Engine 내에서 Sentinel 이미지를 사용하고 이전된 학습 샘플을 사용하는 토지 피복 분류를 위한 새로운 자동 워크플로를 사용하여 이란의 토지 피복 지도를 개선했습니다. ISPRS Journal of
Photogrammetry and Remote Sensing, 167, 276-288.
doi:10.1016/j.isprsjprs.2020.07.013
이란 전역의 토지 피복 지도는 Google Earth Engine Cloud 플랫폼 내에서 Sentinel 이미지를 처리하여 생성되었습니다. 이를 위해 2017년의 단일 모자이크 데이터 세트를 생성하기 위해 2,500개가 넘는 Sentinel-1 이미지와 11,000개가 넘는 Sentinel-2 이미지가 처리되었습니다. 그런 다음 객체 기반 랜덤 포레스트 분류 방법이 학습되었습니다.
[null,null,[],[[["\u003cp\u003eThis dataset provides a 13-class land cover map of Iran for the year 2017, created using Sentinel-1 and Sentinel-2 imagery processed in Google Earth Engine.\u003c/p\u003e\n"],["\u003cp\u003eThe classification was performed using an object-based Random Forest method trained with a large number of reference samples.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available at a 10-meter resolution and includes classes like urban, water, wetland, forest, and farmland.\u003c/p\u003e\n"],["\u003cp\u003eThe data is provided by the K.N. Toosi University of Technology LiDAR Lab and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).\u003c/p\u003e\n"],["\u003cp\u003eUsers can access and analyze this dataset through the Google Earth Engine platform.\u003c/p\u003e\n"]]],["The K. N. Toosi University of Technology LiDAR Lab produced a 13-class land cover map of Iran for 2017, using over 13,500 Sentinel-1 and Sentinel-2 images processed in Google Earth Engine. An object-based Random Forest classification method was applied, with reference samples to identify categories like urban, water, forest, and more. The map is accessible via Earth Engine with the snippet `ee.Image(\"KNTU/LiDARLab/IranLandCover/V1\")`, under a CC BY 4.0 license.\n"],null,["# Iran Land Cover Map v1 13-class (2017)\n\nDataset Availability\n: 2017-01-01T00:00:00Z--2018-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [K. N. Toosi University of Technology LiDAR Lab](https://en.kntu.ac.ir/laser-scanners-laboratory/)\n\nTags\n:\n [landcover](/earth-engine/datasets/tags/landcover) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) \n iran \nkntu \n\n#### Description\n\nThe Iran-wide land cover map was generated by processing Sentinel imagery\nwithin the Google Earth Engine Cloud platform. For this purpose, over 2,500\nSentinel-1 and over 11,000 Sentinel-2 images were processed to produce a single\nmosaic dataset for the year 2017. Then, an object-based Random Forest\nclassification method was trained by a large number of reference samples for 13\nclasses to generate the Iran-wide land cover map.\n\n### Bands\n\n**Bands**\n\n| Name | Pixel Size | Description |\n|------------------|------------|----------------|\n| `classification` | 10 meters | Classification |\n\n**classification Class Table**\n\n| Value | Color | Description |\n|-------|---------|-----------------|\n| 1 | #000000 | Urban |\n| 2 | #006eff | Water |\n| 3 | #41a661 | Wetland |\n| 4 | #ff7f7f | Kalut (yardang) |\n| 5 | #bee8ff | Marshland |\n| 6 | #ff00c5 | Salty Land |\n| 7 | #ff0000 | Clay |\n| 8 | #00734c | Forest |\n| 9 | #732600 | Outcrop |\n| 10 | #ffaa00 | Uncovered Plain |\n| 11 | #d3ffbe | Sand |\n| 12 | #446589 | Farm Land |\n| 13 | #cccccc | Range Land |\n\n### Terms of Use\n\n**Terms of Use**\n\nThis work \"Iran Land Cover Map\nv1 13-class (2017)\" by Arsalan Ghorbanian, Mohammad Kakooei, Meisam Amani,\nSahel Mahdavi, Ali Mohammadzadeh, Mahdi Hasanlou is licensed under the [Creative\nCommons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)\n\n### Citations\n\nCitations:\n\n- Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., \\&\n Hasanlou, M. (2020). Improved land cover map of Iran using Sentinel imagery\n within Google Earth Engine and a novel automatic workflow for land cover\n classification using migrated training samples. ISPRS Journal of\n Photogrammetry and Remote Sensing, 167, 276-288.\n [doi:10.1016/j.isprsjprs.2020.07.013](https://doi.org/10.1016/j.isprsjprs.2020.07.013)\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset = ee.Image('KNTU/LiDARLab/IranLandCover/V1');\n\nvar visualization = {\n bands: ['classification']\n};\n\nMap.setCenter(54.0, 33.0, 5);\n\nMap.addLayer(dataset, visualization, 'Classification');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/KNTU/KNTU_LiDARLab_IranLandCover_V1) \n[Iran Land Cover Map v1 13-class (2017)](/earth-engine/datasets/catalog/KNTU_LiDARLab_IranLandCover_V1) \nThe Iran-wide land cover map was generated by processing Sentinel imagery within the Google Earth Engine Cloud platform. For this purpose, over 2,500 Sentinel-1 and over 11,000 Sentinel-2 images were processed to produce a single mosaic dataset for the year 2017. Then, an object-based Random Forest classification method was trained ... \nKNTU/LiDARLab/IranLandCover/V1, landcover,landuse-landcover \n2017-01-01T00:00:00Z/2018-01-01T00:00:00Z \n24.62 43.46 39.95 65.58 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://en.kntu.ac.ir/laser-scanners-laboratory/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/KNTU_LiDARLab_IranLandCover_V1)"]]