用图神经网络融合邻近区域信息，提升贫民窟地图绘制精度。
Neighbor-aware informal settlement mapping with graph convolutional networks
- 将每个街区及其邻接区域构建成图结构，用轻量GCN识别贫民窟。
- 在里约热内卢五区测试中，卡帕系数比单个单元分类提升17点。
- 适合做城市规划、公共健康等需要精准地理数据的研究者使用。
快速扩张的城市中，绘制非正式聚居区对城市规划、公共卫生和基础设施建设至关重要。遥感数据的地理空间机器学习已成为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，忽视了城市肌理中的关系结构。本文提出一种基于图的框架，将局部地理上下文显式融入分类过程：每个空间单元（网格）与其相邻邻居一起嵌入图结构，并训练轻量级图卷积网络（GCN）判断中心单元是否属于非正式聚居区。在里约热内卢的案例研究中，采用跨五个不同区域的空间交叉验证，确保模型在异质城市景观中的稳健性和泛化能力。实验表明，该方法优于标准基线，卡帕系数提升17点；同时，图建模优于简单的邻域特征拼接，证明编码空间结构对城市场景理解具有显著优势。
原文摘要 · Abstract (English)
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.
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