Landslides are a widely recognized phenomenon, causing huge economic and human losses worldwide. The detection of spatial and temporal landslide deformation, together with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Advanced landslide monitoring systems based on remote sensing techniques (RSTs) play a crucial role in risk management and provide important support for early warning systems (EWSs) at local and regional scales. The purpose of this article is to present a review of the current state of knowledge in the development of RSTs used for identifying landslide precursors, as well as detecting, monitoring, and predicting landslides. Almost 200 articles from 2010 to 2024 were analyzed, in which the authors utilized RSTs to detect potential precursors for early warning of hazards. The applications, challenges, and trends of RSTs, largely dependent on the type of landslide, deformation pattern, hazards posed by the landslide, and the size of the area of interest, were also discussed. Although the article indicates some limitations of the RSTs used so far, integrating different techniques and technological developments offers the opportunity to create reliable EWSs and improve existing ones.

This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions included numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pixels for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the conducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Matthews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs.

Simultaneous localization and mapping (SLAM) is essential for the robot to operate in an unknown, vast environment. LiDAR-based SLAM can be utilizable in environments where other sensors cannot deliver reliable measurements. Providing accurate map results requires particular attention due to deviations originating from the device. This study is aimed to assess LiDAR-based mapping quality in a vast environment. The measurements are conducted on a mobile platform. Accuracy of the map collected with the LeGO-LOAM method was performed by making a comparison to a map gathered with geodetic scanning using ICP. The results provided 60% of fitted points in a distance lower than 5 cm and 80% in a distance lower than 10 cm. The findings prove the mileage of the map created with this method for other tasks, including autonomous driving and semantic segmentation.

In recent years, uncrewed aerial vehicle (UAV)-based photogrammetry has developed rapidly and is increasingly used for monitoring and determining displacements. The presentation discusses the author's solutions for the automatic determination of horizontal and vertical displacements of land surface in urban areas, dedicated to very-high-resolution UAV-photogrammetry products. The processing path is based on orthomosaics and digital elevation models and implements normalized cross-correlation for matching multi-temporal images. Its integral part is the process of semi-automatic removal of outliers. As a result of data processing, displacement vectors are determined in a regular grid, which constitute the basis for determining other indices of terrain deformation, such as ground tilts and horizontal deformations. Based on a comparison with reference data, it was estimated that the root mean square error of determining the displacements is 1-2 pixels for the horizontal components and 2-3 pixels for the vertical component. Therefore, the components of ground tilt and horizontal deformation can be determined based on UAV photogrammetry with a root mean square error of 0.3 pixels.

Detection of spatial and temporal deformations of landslides, along with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Contemporary landslide monitoring systems based on remote sensing techniques (RST) play an important role in risk management and provide important support for Early Warning Systems. Research into the feasibility of using RST for monitoring different types of landslides also includes an analysis of the impact of radar wavelength on the obtained displacement results. The paper compares the time series results of landslide displacements obtained from satellite interferometric imaging in the C-band and L-band. The focus has been particularly on analyzing how the radar wavelength can impact the accuracy of the obtained displacement values and the ability to penetrate dense vegetation, especially under conditions of varying vegetation density. This poses a significant challenge for correct displacement detection. The obtained results are particularly relevant for geographical areas, such as Poland, where a large number of landslides occur in regions covered by dense vegetation. These are the conditions under which scientists encounter the greatest challenges in accurately monitoring these areas using radar systems. The final findings of the research are an important contribution to the development of landslide risk management strategies, crucial for the safety of people and infrastructure.