Journal of Engineering Issues and Solutions Land Cover Segmentation from Satellite Imagery Using U-Net with Custom Loss and Morphological Postprocessing
This project designs and implements a semantic segmentation pipeline using a modified U-Net architecture from scratch for pixel-level classification of satellite images and demonstrates a full end-to-end pipeline from model design to web deployment and sets a foundation for scalable geospatial AI applications tailored for Nepal.
A deep learning-based semantic segmentation framework using the U-Net architecture for pixel-level landslide delineation from multispectral and topographical remote sensing data and establishes a foundation for further investigation of advanced Transformer- and attention-based architectures is presented.
Neeta Bajpai, Rahul Pethe· International journal of com...· 0 citations
Remote sensing image segmentation is essential to extract valuable information from satellite and
aerial images to achieve significant applications such as urban planning and ecological
monitoring. Yet, it is hard to accurately segment diverse and complicated features because of the
constraints of conventional appro...
Chibueze Favour Aririguzo· IIARD International Journal...· 0 citations
Accurate and timely burned area mapping is crucial for post-fire management, planning, and
mitigation strategies. Traditional approaches for burned area detection are often time-
consuming and challenging, highlighting the need for more efficient and automated
methods. This study investigates the performance of several...
Nur Nafi'iyah, Rifky Aisyatul Faroh, Salwa Nabilah et al.· ITEGAM- Journal of Engineeri...· 0 citations
Landslides are among the most destructive natural hazards. High-resolution remote sensing imagery provides an effective means for landslide detection and mapping, supporting geological hazard assessment and early warning. However, existing methods still face challenges in complex mountainous scenes with heterogeneous l...
Results indicate that the proposed HAA-UNet method effectively improves road continuity and boundary delineation in complex forest scenes and is integrated into the Forest Fire Risk Index (FFRI) assessment framework, demonstrating that accurate road data can improve the spatial characterization of fire risk and provide...
Hong-Rong Wang, Hao-Quan Chen, Fei-Fan Yang et al.· Sustainability· 0 citations
This paper examines the application of deep learning methods for the automatic segmentation of satellite images with the aim of
classifying land cover types based on their degree of vegetation cover. Three-channel satellite images and vector polygons annotated by experts
were used as input data, including classes such...
Marina V. Ogneva, P. A. Shlapak, Alyona I. Konopleva· Izvestiya of Saratov Univers...· 0 citations
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