When a natural disaster occurs, humanitarian organizations need to be prompt, effective, and efficient to support people whose security is threatened. Satellite imagery offers rich and reliable information to support expert decision-making, yet its annotation remains labour-intensive and tedious. In this work, we evaluate the applicability of convolutional neural networks (CNN) in supporting building damage assessment in an emergency context. Despite data scarcity, we develop a deep learning workflow to support humanitarians in time-constrained emergency situations. To expedite decision-making and take advantage of the inevitable delay to receive post-disaster satellite images, we decouple building localization and damage classification tasks into two isolated models. Our contribution is to show the complexity of the damage classification task and use established transfer learning techniques to fine-tune the model learning and estimate the minimal number of annotated samples required for the model to be functional in operational situations.

Remote sensing super-resolution aims to enhance the spatial details of satellite images by introducing meaningful high-frequency features while avoiding hallucinations and spectral distortions. High-resolution imagery is usually not publicly available, whereas low-resolution imagery is freely available with a much higher revisit rate, such as the Sentinel-2 multispectral imaging mission. Cross-sensor super-resolution has the potential to bridge this gap, providing high spatial and temporal resolution imagery which are otherwise unavailable for many remote sensing users and applications. With the recent advancements in diffusion models, many methodologies have emerged which take advantage of their generative power to perform super-resolution. We propose an adapted latent diffusion approach, since image diffusion is computationally prohibitive to be applied to large Earth observation datasets. Contrary to standard latent diffusion, we encode the low-resolution image to condition the diffusion process, forcing better spectral consistency with the input imagery. The model includes visible and near-infrared bands. To ensure trustworthy results, we utilize the probabilistic nature of diffusion models to generate pixel-level uncertainty maps. This confidence metric is crucial for real-world applications, such as environmental monitoring, land cover classification, and change detection, where accurate surface feature reconstruction and spectral consistency are essential. The uncertainty map allows users to evaluate the reliability of the product for these tasks. The proposed model super-resolves Sentinel-2 imagery at 10 to 2.5 m and is the first multispectral remote sensing (RS) super-resolution diffusion model efficient enough to process large-scale RS datasets, as well as the only model providing a pixelwise uncertainty metric.

In recent years, there has been a growing interest in using image super-resolution (SR) techniques in remote sensing. These techniques aim to reconstruct high-resolution (HR) imagery from low-resolution (LR) sources. Despite the development of sophisticated SR methodologies, determining what constitutes “good” SR is still a matter of debate. Present-day literature often presents SR models through a strong computer vision perspective, heavily relying on synthetic datasets. Moreover, commonly used metrics often prioritize attributes that do not necessarily correspond to improvements in spatial resolution. To address this challenge, we present OpenSR-test, a comprehensive benchmark designed exclusively for evaluating SR of remote sensing images. Our framework incorporates specific quality metrics and curated cross-sensor datasets, each spanning various scale factors with consistent metadata. Utilizing OpenSR-test, we evaluate the state-of-the-art SR algorithms from a remote sensing perspective. The OpenSR-test framework and datasets are publicly available at https://esaopensr.github.io/opensr-test/ .

There is a critical need for a global monitoring capability for Tailings Storage Facilities (TSFs), to help protect the surrounding communities and the environment. Satellite Synthetic Aperture Radar Interferometry (InSAR) shows much promise towards this ambition. However, extracting meaningful information and interpreting the deformation patterns from InSAR data can be a challenging task. One approach to address this challenge is through the use of data science techniques. In this study, the representation of InSAR metadata as Entity Embeddings within a Deep Learning framework (EE-DL) is investigated for modelling the spatio-temporal deformation response. Entity embeddings are commonly used in natural-language-processing tasks. They represent discrete objects, such as words, as continuous, low-dimensional vectors that can be manipulated mathematically. We demonstrate that EE-DL can be used to predict anomalous patterns in the InSAR time series. To evaluate the performance of the EE-DL approach in SAR interferometry, we conducted experiments over a mining test site (Cadia, Australia), which has been subject to a TSF failure. This study demonstrated that EE-DL can detect and predict the fine spatial movement patterns that eventually resulted in the failure. We also compared the results with deformation predictions from common baseline models, the Random Forest model and Gaussian Process Regression (GPR). Both EE-DL and GPR greatly outperform Random Forest. While GPR is also able to predict displacement patterns with millimetric accuracy, it detects a significantly lower number of anomalies compared to EE-DL. Overall, our study showed that EE-DL is a promising approach for building early-warning systems for critical infrastructures that use InSAR to predict ground deformations.