融合雷达与多光谱影像，精准识别阿根廷科尔多瓦的非正规聚居区。
Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in C\'ordoba, Argentina
- 用多源遥感数据融合方法，构建非正规聚居区识别模型。
- 晚期融合+高光谱数据使分类准确率最优，空间定位更精准。
- 发现官方名录外区域也存在显著热岛效应，适合城市规划者参考。
非正规聚居区是快速扩张城市面临的主要挑战，但因其外观多样且官方记录不全，从地球观测数据中识别仍具难度。本文针对阿根廷科尔多瓦市，提出一种融合高分辨率PlanetScope多光谱（MS）、COSMO-SkyMed合成孔径雷达（SAR）及中分辨率PRISMA高光谱（HS）数据的深度学习框架，用于非正规聚居区风险映射。任务设定为像素块级分类，以官方注册的ReNaBaP名录为标注依据，通过四个地理分区交叉验证模型性能。系统比较了仅用SAR或仅用MS的基线模型，以及加入PRISMA HS支持后的表现，还测试了早期融合（EF）、中期融合（MF）和晚期融合（LF）策略。结果表明，LF+HS组合在分类性能与空间选择性之间取得最佳平衡，且PRISMA提供互补的光谱信息，增强对高分辨率MS与SAR特征的理解。除基于ReNaBaP的标准评估外，引入外部市政脆弱性图层分析未被官方收录的疑似区域，发现多个看似误报的点实际位于更广泛的脆弱城区内。热力分析显示，ReNaBaP聚居区在热浪期间地表温度显著高于周边环境，揭示局部热放大效应。综合来看，多源遥感融合有助于精确绘制官方名录中的聚居区，并揭示更广泛的城区脆弱性模式。
原文摘要 · Abstract (English)
Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their heterogeneous appearance and incomplete official inventories. This work presents a multi-sensor deep learning (DL) framework for slum-likelihood mapping in C\'ordoba, Argentina, integrating high-resolution PlanetScope multispectral (MS) imagery, COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) data, and medium-resolution PRISMA hyperspectral (HS) observations. The problem is formulated as a patch-level classification task using the official Registro Nacional de Barrios Populares (ReNaBaP) inventory as reference, and the models are evaluated through four geographically partitioned folds. SAR-only and MS-only baselines, their configurations with PRISMA HS support, and early fusion (EF), middle fusion (MF), and late fusion (LF) strategies are systematically compared. Results show that LF+HS provides the best overall balance between classification performance and spatial selectivity, while PRISMA contributes complementary spectral information alongside the higher-resolution MS and SAR representations. Beyond the standard evaluation against ReNaBaP, an external municipal vulnerability layer is used to interpret detections outside the official polygons, showing that several apparent false positives overlap broader vulnerable urban areas. Thermal analysis further shows that ReNaBaP settlements exhibit significantly higher surface temperatures than their immediate surroundings during a heatwave event, indicating localised surface-heat amplification. Taken together, these results suggest that multi-sensor EO fusion can support both the mapping of ReNaBaP settlements and the interpretation of broader urban vulnerability patterns.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。