Rethink Geographical Generalizability with Unsupervised Self-Attention Model Ensemble: A Case Study of OpenStreetMap Missing Building Detection in Africa

Abstract

The recent advance of adapting pre-trained task-agnostic artificial intelligence (AI) models leads to great successes in downstream tasks via fine-tuning, or low-resource (i.e., few-shot and zero-shot) learning. However, when adapting such pre-trained AI models to geographical applications, it is still challenging to find the "sweet spot"of the model's generalizability and specializability (e.g., geographic generalizability v.s. spatial heterogeneity). For instance, a building detection task may require vision models with different parameters across different geographic areas of the world. In this paper, we rethink this interesting topic, namely Geographical Generalizability of GeoAI models, with a case study of detecting OpenStreetMap (OSM) missing buildings across different countries in sub-Saharan Africa. We consider a real-world scenario, in which we first train a Single-Shot Multibox Detection (SSD) base model for OSM missing building detection in Kakola, Tanzania, where a previous humanitarian mapping project of OSM was organized to map all possible buildings. Then we extrapolate this base model using Few-Shot Transfer Learning (FSTL) to a set of areas in the proximity of the test area in Cameroon. Here, we develop a Geographical Weighted Model Ensemble (GWME) method to improve Geographical Generalizability of GeoAI models. Moreover, we compare four unsupervised model ensemble weighting strategies: 1) Average weighting, 2) Image similarity weighting, 3) Geographical distance weighting, and 4) Self-attention-based weighting. Experiments show promising results of the proposed GWME method, which implicitly generates model weights from their location embedding and image feature embedding in an unsupervised manner. More specifically, the self-attention-based model ensemble achieves the highest performance. The results shed inspiring light on improving the generalizability and replicability of GeoAI models across geographic areas. Data and code are available at https://github.com/tum-bgd/GWME.