Mapping informal settlements is crucial for addressing challenges related to urban planning, public health, and infrastructure in rapidly growing cities. Geospatial machine learning has emerged as a key tool for detecting and mapping these areas from remote sensing data. However, existing approaches often treat spatial units independently, neglecting the relational structure of the urban fabric. We propose a graph-based framework that explicitly incorporates local geographical context into the classification process. Each spatial unit (cell) is embedded in a graph structure along with its adjacent neighbors, and a lightweight Graph Convolutional Network (GCN) is trained to classify whether the central cell belongs to an informal settlement. Experiments are conducted on a case study in Rio de Janeiro using spatial cross-validation across five distinct zones, ensuring robustness and generalizability across heterogeneous urban landscapes. Our method outperforms standard baselines, improving Kappa coefficient by 17 points over individual cell classification. We also show that graph-based modeling surpasses simple feature concatenation of neighboring cells, demonstrating the benefit of encoding spatial structure for urban scene understanding.

翻译：非正规住区制图对于解决快速扩张城市中城市规划、公共卫生和基础设施相关挑战至关重要。地理空间机器学习已成为从遥感数据中检测和绘制这些区域的关键工具。然而，现有方法通常将空间单元独立处理，忽略了城市肌理的关系结构。我们提出一种基于图的框架，将局部地理上下文显式地纳入分类过程。每个空间单元（像元）与其相邻单元共同嵌入图结构中，并训练一个轻量级图卷积网络（GCN）以判断中心像元是否属于非正规住区。我们在里约热内卢的案例研究中进行了实验，采用跨越五个不同区域的空间交叉验证，确保了方法在异质城市景观中的鲁棒性与泛化能力。本方法优于标准基线模型，其Kappa系数较单像元分类提升了17个百分点。我们还证明，基于图的建模方法优于简单的邻域像元特征拼接，这验证了编码空间结构对城市场景理解的益处。