Rainstorm-induced landslides are a widespread geomorphological hazard that can lead to major emergencies, causing severe damage to life and property. Due to the extent of the areas usually affected by these phenomena (up to thousands of km2) and/or their typical high areal density, in the early stages of the emergency it can be useful to reconstruct a comprehensive, albeit preliminary, overview…
Rockfall is considered the main geohazard in mountainous areas with steep morphology. The main objective of this study is to assess the rockfall hazard in the cultural heritage site of the Monastery of Agia Paraskevi, Monodendri, in northern Greece, where a recent rockfall event occurred, destroying a small house and the protective fence constructed to protect the Monastery of Agia Paraskevi. T…
Tightly-coupled sensor orientation, i.e. the simultaneous processing of temporal (GNSS and raw inertial) and spatial (image and lidar) constraints in a common adjustment, has demonstrated significant improvement in the quality of attitude determination with small inertial sensors. This is particularly beneficial in kinematic laser scanning on lightweight aerial platforms, such as drones, which …
Leveraging ground-annotated data for scene analysis on unmanned aerial vehicles (UAVs) can lead to valuable real-world applications. However, existing unsupervised domain adaptive (UDA) methods primarily focus on domain confusion, which raises conflicts among training data if there is a huge domain shift caused by variations in observation perspectives or locations. To illustrate this problem, …
3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities; although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, includi…
Real-time object detection and tracking is an active area of aerial remote sensing research that enables many environmental and ecological monitoring and preservation applications. Despite the development of several solutions tailored for these specific applications, trade-offs between cost efficiency and feature richness persist. This paper proposes a lightweight, low-cost, and modular approac…
Depth estimation and 3D model reconstruction from aerial imagery is an important task in photogrammetry, remote sensing, and computer vision. To compare the performance of different image-based approaches, this study presents a benchmark for UAV-based aerial imagery using the UseGeo dataset. The contributions include the release of various evaluation routines on GitHub, as well as a comprehensi…
Predicting crop yield using deep learning (DL) and remote sensing is a promising technique in agriculture. In smallholder agriculture (
UAV-mounted sensors can be used to estimate crop biophysical traits, offering an alternative to traditional field scouting. However, the high temporal resolution offered by UAV platforms, critical for identifying small differences in crop conditions, is rarely exploited throughout the entire growing season. This limits growers' ability to obtain timely information for real-time interventions. N…
The cover and management factor (C-factor) of the Universal Soil Loss Equation (USLE) represents the effects of crop cover, weighted by rainfall pattern, on predicted soil erosion rates. This requires an estimate of seasonal rainfall erosivity and soil protection afforded by the crop at different phenological stages, expressed by a soil loss ratio (SLR). However, soil erosion modelers often rel…