MO3.R6.7

AUTOMATING CROP-FIELD SEGMENTATION IN HIGH-RESOLUTION SATELLITE IMAGES: A U-NET APPROACH WITH OPTIMIZED MULTITEMPORAL CANNY EDGE DETECTION

Alvise Ferrari, Simone Saquella, Giovanni Laneve, Valerio Pampanoni, Sapienza University, Italy
Session:
MO3.R6: Mapping Cropland and Land Use Oral
Track:
Land Applications
Location:
Skalkotas Hall
Presentation Time:
Mon, 8 Jul, 15:04 - 15:18
Session Co-Chairs:
Saeid Homayouni, and Clémence Dubois ,
Presentation
Discussion
Resources
No resources available.
Session MO3.R6
MO3.R6.1: CROPLAND RECOGNITION BASED ON COLLABORATIVE SPATIAL ATTENTION AND EDGE DETECTION FOR MULTI-SOURCE REMOTE SENSING DATA
Minghui Chang, Shihua Li, Hao Tang, University of Electronic Science and Technology of China, China; Tao Zhao, Yu Mu, Gang Qin, Technology Innovation Center for Southwest Land Space Ecological Restoration and Comprehensive Renovation, Ministry of Natural Resources, China
MO3.R6.2: INTRODUCING THE POTENTIAL OF THE NEW ENMAP-BOX HYBRID RETRIEVAL WORKFLOW FOR QUANTIFYING NON-PHOTOSYNTHETIC VEGETATION
Stefanie Steinhauser, Matthias Wocher, Ludwig-Maximilian University Munich, Germany; Andrej Halabuk, Institute of Landscape Ecology, Slovakia; Svetlana Košánová, Department of Ecology and Environmental Science, Slovakia; Tobias Hank, Ludwig-Maximilian University Munich, Germany
MO3.R6.3: A DEEP LEARNING DATA FUSION APPROACH FOR MODELING LAND USE IN SMALLHOLDER AGRICULTURE SYSTEMS
Margaret Wooten, Science Systems and Applications, Inc., United States; Jordan Caraballo-Vega, NASA Goddard Space Flight Center, United States; Nathan Thomas, Edge Hill University, United Kingdom; William Wagner, Science Systems and Applications, Inc., United States; Christopher Neigh, Mark Carroll, NASA Goddard Space Flight Center, United States; Molly Brown, University of Maryland, United States; Abdoul Aziz Diouf, Centre de Suivi Écologique, Senegal; Modou Mbaye, Institut Sénégalais de Recherches Agricoles, Senegal; Babacar Ndao, Centre de Suivi Écologique, Senegal; Konrad Wessels, George Mason University, United States; Woubet Alemu, University of Maryland, United States
MO3.R6.4: CLOUD-POWERED AGRICULTURAL MAPPING: A REVOLUTION TOWARD 10M RESOLUTION CROPLAND DATA LAYERS
Zhe Li, Rick Mueller, Zhengwei Yang, David Johnson, Patrick Willis, USDA - National Agricultural Statistics Service, United States
MO3.R6.5: DEEP LEARNING BASED CENSUS AND MAPPING OF OIL PALM PLANTATIONS FROM VERY HIGH RESOLUTION SATELLITE IMAGES
Niranjan Dilip Gholba, Anil Kumar, University of Petroleum and Energy Studies (UPES), India
MO3.R6.6: CROPLAYER-CHINA: A 2-METER RESOLUTION CROPLAND MAP OF CHINA BASED ON ACTIVE LEARNING OF SEGMENTATION WITH MAPBOX AND GOOGLE SATELLITE IMAGERY
Hao Jiang, Xia Zhou, Mengjun Ku, Jianhui Xu, Xuemei Dai, Chongyang Wang, Dan Li, Jiayi Wei, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China, China
MO3.R6.7: AUTOMATING CROP-FIELD SEGMENTATION IN HIGH-RESOLUTION SATELLITE IMAGES: A U-NET APPROACH WITH OPTIMIZED MULTITEMPORAL CANNY EDGE DETECTION
Alvise Ferrari, Simone Saquella, Giovanni Laneve, Valerio Pampanoni, Sapienza University, Italy
Resources
No resources available.