Deep Learning Guided Crop Boundary Delineation from Multimodal Satellite Images (Furkan Celik)

Earth Observation Summer School 2026 17 Aug 2026 – 21 Aug 2026, Istanbul, Turkey https://opengeohub.org/events/earth-observation-summer-school-2026 Crop field boundary delineation from satellite imagery is a core requirement for scalable agricultural monitoring, yet it is constrained by cloud contamination, within field spectral variability, fragmented crop boundary structure, and weak edge contrast in smallholder landscapes. This lecture introduces a deep learning approach for boundary focused segmentation using multimodal satellite data, emphasizing how complementary sensors jointly improve edge detectability. The session explains how optical multispectral imagery contributes spectral separability while SAR provides structure sensitive signals that remain available under clouds. We then present model designs that move from simple early fusion baselines to dual-stream encoder–decoder networks with attention mechanisms that highlight boundary relevant features. Finally, we describe training strategies and boundary aware objectives that explicitly strengthen boundaries and reduce over smoothing, followed by evaluation and inference pipelines that transform patch-level predictions into seamless, end-to-end boundary products.