Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning

Abstract: The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of widely available remote sensing data for the prediction of infrastructure quality in Africa. We train a convolutional neural network to predict ground truth labels from the Afrobarometer Round 6 survey using Landsat 8 and Sentinel 1 satellite imagery. Our best models predict infrastructure quality with AUROC scores of 0.881 on Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using Landsat 8. These performances are significantly better than models that leverage OpenStreetMap or nighttime light intensity on the same tasks. We also demonstrate that our trained model can accurately make predictions in an unseen country after fine-tuning on a small sample of images. Furthermore, the model can be deployed in regions with limited samples to predict infrastructure outcomes with higher performance than nearest neighbor spatial interpolation.

• The paper presents a deep learning approach using ResNet to predict infrastructure quality in Africa by integrating Landsat 8 and Sentinel 1 satellite imagery.
• The methodology utilizes transfer learning with a pre-trained ResNet for multi-label binary classification, achieving AUROC scores above 0.85 for key infrastructure metrics.
• Results demonstrate the model's potential to supplement costly field surveys and enable automated monitoring of infrastructure in under-surveyed regions.

Analysis of "SIG Proceedings Paper in LaTeX Format"

The paper under review presents an ambitious approach toward infrastructure quality mapping through deep learning models applied to satellite imagery. Utilizing Landsat 8 and Sentinel 1 data, the research explores the potential to predict infrastructure outcomes across African nations, addressing a significant data deficit in developing regions. This endeavor is notable for employing convolutional neural networks (CNNs) to bridge existing gaps in field survey data, which are often costly and logistically challenging to collect.

Methodology and Model Implementation

The methodological framework of this study capitalizes on a deep learning architecture, specifically Residual Neural Networks (ResNet), to classify and predict various infrastructure quality metrics. Training involved the fine-tuning of a pre-trained ResNet via transfer learning, adapting it to process the spectral bands unique to satellite images provided by Landsat 8 and Sentinel 1. The researchers achieved a sophisticated problem formulation, categorizing the task into a multi-label binary classification scenario. The infrastructure outputs were binary labels, and the models optimized their parameters through mean binary cross-entropy loss.

Data Sources and Experimentation

The study leverages diverse data sources:

1. Afrobarometer Round 6: Used as ground truth, this extensive survey provided critical labels for infrastructure quality spanning multiple African nations.
2. Satellite Imagery: With 30m resolution from Landsat 8 and 10m from Sentinel 1, the study scrutinizes the potential of varying spatial resolutions to discern infrastructure attributes.

Experimental results demonstrated promising AUROC scores, particularly for electricity and sewerage, with scores above 0.85, outstripping benchmarks set by nightlights and OpenStreetMap (OSM) baselines. This suggests highly effective model predictions correlating with urban infrastructure density visible within satellite imagery.

Evaluation and Generalization

Through meticulous analysis under different baselines (nightlights, OSM, and spatial interpolation), the researchers validated their model's superior performance. However, the generalization capabilities highlight a critical challenge: geographic and infrastructural diversity across nations affects predictive accuracy. Notably, the paper ascertains that, while immediate generalization to unforeseen countries shows declined accuracy, a small fine-tuned sample can regain predictive prowess.

Implications and Future Prospects

The implications of this research extend to potential applications for monitoring infrastructure development, policy planning, and automated data supplementation for under-surveyed regions. The exploration into high-resolution imagery and integrated multi-source satellite data could further amplify model precision. By continuing to enhance data availability and harness transfer learning from diverse domains, the approach can progressively tackle temporal and spatial generalization issues.

Continued progress in this area prompts optimism for automated geospatial analysis tools that can efficiently assess infrastructure in regions where traditional data collection is not feasible. Future work could align with temporal analysis of infrastructure changes, higher resolution imagery utilization, and richer feature integration from alternative data sources like ground-level geospatial data.

This research presents a robust framework with significant contributions to socio-economic applications and the broader field of remote sensing and AI in development.