The detection and characterization of illegal solid waste disposal sites are essential for environmental protection, particularly for mitigating pollution and health hazards. Improperly managed landfills contaminate soil and groundwater via rainwater infiltration, posing threats to both animals and humans. Traditional landfill identification approaches, such as on-site inspections, are time-consuming and expensive. Remote sensing is a cost-effective solution for the identification and monitoring of solid waste disposal sites that enables broad coverage and repeated acquisitions over time. Earth Observation (EO) satellites, equipped with an array of sensors and imaging capabilities, have been providing high-resolution data for several decades. Researchers proposed specialized techniques that leverage remote sensing imagery to perform a range of tasks such as waste site detection, dumping site monitoring, and assessment of suitable locations for new landfills. This review aims to provide a detailed illustration of the most relevant proposals for the detection and monitoring of solid waste sites by describing and comparing the approaches, the implemented techniques, and the employed data. Furthermore, since the data sources are of the utmost importance for developing an effective solid waste detection model, a comprehensive overview of the satellites and publicly available data sets is presented. Finally, this paper identifies the open issues in the state-of-the-art and discusses the relevant research directions for reducing the costs and improving the effectiveness of novel solid waste detection methods.

Wilderness Search and Rescue (WSAR) missions are time-critical emergency response operations that require locating a lost person within a short timeframe. Large forested terrains must be explored in challenging environments and adverse conditions. Unmanned Aerial Vehicles (UAVs) equipped with thermal cameras enable the efficient exploration of vast areas. However, manual analysis of the huge amount of collected data is difficult, time-consuming, and prone to errors, increasing the risk of missing a person. This work proposes an object detection and tracking pipeline that automatically analyzes UAV thermal videos in real-time to identify lost people in forest environments. The tracking module combines information from multiple viewpoints to suppress false alarms and focus responders’ efforts. In this moving camera scenario, tracking performance is enhanced by introducing a motion compensation module based on known camera poses. Experimental results on the collected thermal video dataset demonstrate the effectiveness of the proposed tracking-based approach by achieving a Precision of 90.3% and a Recall of 73.4%. On a dataset of UAV thermal images, the introduced camera alignment technique increases the Recall by 6.1%, with negligible computational overhead, reaching 35.2 FPS. The proposed approach, optimized for real-time video processing, has direct application in real-world WSAR missions to improve operational efficiency.

Illegal waste disposal has direct consequences on people’s quality of life in affected areas. Exposure to hazardous waste substances has been linked to increased rates of multiple forms of cancer. Traditional inspection methods require time and manpower. Advances in UAV technology, combined with AI-enabled Computer Vision, offer a promising solution to significantly reduce survey time and the required workforce. The main contributions of this work are the evaluation of two Object Detection models, YOLOv8 and Faster R-CNN, trained to identify 7 waste materials from UAV imagery and the presentation of a practical pipeline to implement the proposed model in environmental agency workflows. To the best of our knowledge, no existing dataset or model has been designed to detect such a diverse range of waste types from UAV images. Results suggest that Object Detection is highly effective for regularly shaped waste categories such as Textile, Pallets and Tires, with the best YOLOv8 model achieving AP scores of 80.22%, 69.24% and 62.47% respectively. However, for waste materials with irregular boundaries, such as Rubble or Mixed Items, detection remains challenging. These findings provide valuable insights for future research in AI-driven environmental crime detection.

Monitoring urban biodiversity is essential for designing resilient and sustainable cities. Urban trees provide a wide range of ecosystem services (ESs), including air pollution reduction, urban heat island mitigation, and psychological benefits for citizens. Accurate and updated tree inventories are therefore essential tools for urban environmental monitoring. However, existing urban tree inventories are often incomplete or outdated, especially in private areas, limiting accurate ES assessment and urban planning. Earth observation satellite missions, particularly very-high-resolution multispectral (VHR-MS) imagery, offer a valuable alternative to field surveys for gathering information on urban environments. This work proposes a deep learning (DL) framework based on VHR-MS satellite imagery for the automatic generation of accurate urban tree inventories. DL models reduce human effort and save operational time by automatically learning complex representations and patterns from satellite imagery. The proposed encoder–decoder architecture extends prior point-based detection approaches by integrating a ResNet-50 backbone and a percentile-based threshold calibration procedure. Given the lack of suitable training data covering heterogeneous and densely vegetated urban environments, a dedicated dataset was constructed from VHR-MS satellite imagery acquired over the Lombardy region (Italy). The dataset encompasses a wide range of land uses and land covers, including residential and industrial zones, public parks, private gardens, and agricultural areas. Through the photointerpretation of more than 2800 images, precise coordinates for more than 50,000 manually annotated trees were obtained. The DL model is trained with point-level annotations, enabling precise localization of individual trees while reducing annotation ambiguity in dense urban contexts. On the Lombardy dataset at 30 cm/px resolution, the proposed framework achieves 86.72% Precision, 66.92% Recall, an F1-score of 75.54%, and a localization error of 1.473 m.