Chapter Artificial Intelligence Data Science Methodology for Earth Observation
This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligen...
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InTechOpen
2019
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| 001 | doab_20_500_12854_70237 | ||
| 020 | |a intechopen.86886 | ||
| 024 | 7 | |a 10.5772/intechopen.86886 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a U |2 bicssc | |
| 100 | 1 | |a Schwarz, Gottfried |4 auth | |
| 700 | 1 | |a Lorenzo, Jose |4 auth | |
| 700 | 1 | |a Castel, Fabien |4 auth | |
| 700 | 1 | |a Datcu, Mihai |4 auth | |
| 700 | 1 | |a Octavian Dumitru, Corneliu |4 auth | |
| 245 | 1 | 0 | |a Chapter Artificial Intelligence Data Science Methodology for Earth Observation |
| 260 | |b InTechOpen |c 2019 | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/3.0/ |2 cc |4 https://creativecommons.org/licenses/by/3.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Computing & information technology |2 bicssc | |
| 653 | |a Earth observation, machine learning, data mining, Copernicus Programme, TerraSAR-X | ||
| 773 | 1 | 0 | |0 OAPEN Library ID: ONIX_20210602_10.5772/intechopen.86886_420 |7 nnaa |
| 856 | 4 | 0 | |a www.oapen.org |u https://library.oapen.org/bitstream/20.500.12657/49306/1/67593.pdf |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/70237 |7 0 |z DOAB: description of the publication |