A Scientometric and Literature Analysis of Deep Learning-Based Semantic Segmentation in Remote Sensing (2015–2025) (Informatica, 2026)
Semantic segmentation of remote sensing images has advanced rapidly, enabling applications in land cover mapping, disaster response, and urban monitoring. This study presents a hybrid scientometric and literature-based analysis of 733 Scopus-indexed publications (2015–2025). Results show a 29.24% annual growth rate, with China, the United States, and Germany as leading contributors and Wuhan University as the most prolific institution. Research output peaked in 2023, driven by transformers and hybrid architectures such as SegFormer, Mask2Former, and Swin Transformer, which outperform CNN baselines. Citation and keyword analyses reveal two core directions: applied geospatial tasks (land cover, urban analysis, disaster management) and computational advances (CNNs, transformers, domain adaptation). While foundational works remain highly cited, emerging models emphasize efficiency, multimodal fusion, and generalization. Persistent challenges include dataset imbalance, cross-domain adaptation, and lack of standardized benchmarks. By combining bibliometric mapping with methodological synthesis, this study consolidates research trends and highlights future directions in multimodal learning, explainable AI, and robust, scalable segmentation frameworks.