Othmane Echchabi

Yuandong Zhang, Othmane Echchabi, Tianshu Feng et al.

Transportation mode detection is an important topic within GeoAI and transportation research. In this study, we introduce SpeedTransformer, a novel Transformer-based model that relies solely on speed inputs to infer transportation modes from dense smartphone GPS trajectories. In benchmark experiments, SpeedTransformer outperformed traditional deep learning models, such as the Long Short-Term Memory (LSTM) network. Moreover, the model demonstrated strong flexibility in transfer learning, achieving high accuracy across geographical regions after fine-tuning with small datasets. Finally, we deployed the model in a real-world experiment, where it consistently outperformed baseline models under complex built environments and high data uncertainty. These findings suggest that Transformer architectures, when combined with dense GPS trajectories, hold substantial potential for advancing transportation mode detection and broader mobility-related research.

Othmane Echchabi, Aya Lahlou, Nizar Talty et al.

Clean water and sanitation are essential for health, well-being, and sustainable development, yet significant global disparities persist. Although the United Nations' Sustainable Development Goal (SDG) 6 clearly defines targets for universal access to clean water and sanitation, limitations in data coverage and openness impede accurate tracking of progress in many countries. To bridge these gaps, this study integrates Afrobarometer survey data, satellite imagery from Landsat 8 and Sentinel-2, and advanced deep learning techniques using Meta's self-supervised Distillation with No Labels (DINO) model to develop a modeling framework for evaluating access to piped water and sewage system across diverse African regions. The modeling framework achieved notable accuracy, with over 96% for piped water and 97% for sewage system access classification. When combined with geospatial population data, validation against official statistics from the United Nations Joint Monitoring Program demonstrated high concordance at the national scale (R2 of 0.95 for piped water access and R2 of 0.85 for sewage system access). The national-level estimates can represent SDG Indicators 6.1.1 and 6.2.1. This approach provides policymakers and stakeholders with an effective, scalable, and cost-efficient tool to pinpoint underserved areas requiring targeted intervention. The methodology developed herein can be adapted for assessing other infrastructure-related SDGs, promoting enhanced monitoring and informed decision-making towards achieving global sustainability objectives.