Floods are among the most devastating natural disasters, causing severe human and economic losses. Their occurrence frequency has been increasing progressively. Hence, effective and reliable flood analysis is essential for mitigating catastrophic losses. In this regard, Synthetic Aperture Radar (SAR) satellites are invaluable tools for providing large-scale images aimed at flood mapping under all weather conditions. Methods specifically designed for SAR image-based flood mapping are commonly developed under the assumption that the training (source) and test (target) data are sampled from the same distribution. However, in many real-world scenarios, these distributions often differ due to factors such as geographic location and incident angle depending on the satellite, leading to distribution shifts (a.k.a. domain shift), which ultimately degrades model performance. In this study, we investigate domain generalization approaches in combination with segmentation networks for the purpose of SAR based flood mapping. During the model’s training, each flood event is treated as a distinct source domain, with the objective of minimizing the domain shift among them to obtain a more robust model for unseen flooding events. Experiments conducted on the Sen1Floods11 dataset demonstrates an improvement in segmentation performance, with domain generalization approaches 3% in terms of IoU and F1 scores.
Recent advancements in computer vision have significantly improved slum detection using very-high-resolution satellite imagery. However, current algorithms are limited to identifying slums based on single-temporal labels and lack the capability to perform multi-temporal analysis. Here we present a supervised learning model trained on multi-temporal labels, specifically designed to maintain consistent performance across temporal variations. We evaluate our model against baseline approaches trained on singe-temporal labels. Case studies in two cities, Caracas and Karachi, demonstrate that incorporating additional temporal satellite imagery during training produces more consistent and reliable results for multi-temporal analysis. Our study suggests new ways to leverage temporal data in slum detection to effectively monitor urban poverty and track dynamics of informal settlements over time.
Remote sensing plays a critical role in assessing urban landscapes. Despite advances in spatial and temporal resolution, satellite-based remote sensing alone cannot capture fine-scale variations in aspects such as temperature, humidity, air quality and others. This shows a need for in-situ measurements to accompany satellite data, which cannot always be satisfied by the limited network of existing fixed measuring stations. This paper introduces a cargo bicycle outfitted with environmental sensors to address this gap, offering high spatial and temporal resolution for in-situ measurements. We detail the design and implementation of the cargo bike with its sensors as well as the data processing systems developed for this purpose. The results of a measurement campaign conducted in June 2024 demonstrate the system’s effectiveness. This approach enables a more comprehensive understanding of urban environmental dynamics, paving the way for enhanced environmental monitoring in cities.
Urbanization, characterized by physical growth and infrastructure expansion, remains a global phenomenon with profound economic, environmental, and social implications. Traditional methods of monitoring construction activity—key indicators of urban and economic development— often suffer from delays and inconsistencies in reporting. This study introduces a Satellite-based Building Activity Indicator (SBAI) leveraging Sentinel-2 imagery and machine learning to provide highly resolved, spatiotemporal data on urban construction activities. The SBAI identifies new urban developments with high accuracy and highlights the ability to track seasonal and project-specific construction trends. Comparison with official statistics on construction activity validates its reliability, while its granularity offers enhanced insights into intra-year variations and localized urban growth dynamics. The SBAI demonstrates significant potential as a tool for national statistical frameworks, offering timely and detailed data to support responsive and informed decision-making at local and national levels.
Landslides are hazardous phenomena that pose considerable threats to the environment and urban infrastructure. This study focuses on both detecting landslides from remote sensing imagery and evaluating the impact of landslides on urban infrastructure. For landslide detection, we implement a machine learning technique, i.e., Support Vector Machines (SVM) and deep-learning U-Net, in the Pomarico region of Italy. Sentinel-2 satellite data from 2019 were utilized for the analysis, with ground control points collected from the study area to validate the techniques’ results. The findings demonstrated that the SVM and U-Net achieved overall accuracies of 87.00% and 91.00% in detecting landslides, respectively. Additionally, the study revealed that landslides displaced electrical poles across the region, caused significant damage to several buildings, and disrupted the existing road network. In summary, these results provide valuable insights for urban planners and administrators, helping to enhance the effectiveness of methods used to simulate landslides and assess their impact on urban infrastructure.
