The significant increase in the amount of satellite data in recent years along with the increase in computing resources has opened up new possibilities for Earth Observation (EO) data analysis. However, the main obstacle to obtaining highquality products is the limited amount of labeled data. Among the methods that can help solve this type of problem, transfer learning (TL) has gained enormous interest in recent years. It involves using a model trained on one problem to solve another problem. This article reviews TL methods as applied to EO data analyses. It provides a structured overview of methods in relation to applications, publication statistics, and summarizes the advantages and disadvantages noted of previous research.

The dynamic development of deep learning methods in recent years has prompted the widespread application of these algorithms in the field of photogrammetry and remote sensing, especially in the areas of image recognition, classification, and object detection. Still, one of the biggest challenges in this field is the low availability of training datasets, especially regarding applications of oblique aerial imagery and UAV data. The process of acquiring such databases is labor-intensive. The solution to the problem of the unavailability of datasets and the need for manual annotation is to automate the process of generating annotations for images. One such approach is used in the following work. The proposed methodology for semi-automating the creation of training datasets was applied to detect objects on nadir and oblique images acquired from UAV. The methodology includes the following steps: (1) the generation of a dense 3D point cloud by two different methods: UAV photogrammetry and TLS (terrestrial laser scanning); (2) data processing, including clipping to objects and filtering of point clouds; (3) the projection of cloud points onto aerial images; and (4) the generation of bounding boxes bounding the objects of interest. In addition, the experiments performed are designed to test the accuracy and quality of the training datasets acquired in the proposed way. The effect of the accuracy of the point cloud extracted from dense UAV image matching on the resulting bounding boxes extracted by the proposed method was evaluated.

The aim of this study was to assess the quality of images from different UAV sensors in terms of their optical resolution, expressed by the MTF10 parameter. The study was conducted for four cameras: Mavic 3E, Matrice 4E, Phantom 4 RTK and Zenmuse P1, under different weather and lighting conditions, with varying flight altitudes and ISO settings. The Siemens Star pattern and ResolvingPower software provided by The German Aerospace Center (DLR) were used in the analysis. The results showed a significant effect of flight altitude, time of day and ISO sensitivity on image quality. A decrease in sharpness (MTF10) was observed with increasing distance from the image centre and at higher ISO values. The conclusions of the study may be helpful in planning photogrammetric missions using UAVs, enabling better-quality image data.

Benchmarking is an essential tool for scientific and technological progress. This article reviews the benchmarks for 3D point cloud segmentation and classification. Based on the analysis of the articles and the knowledge gathered, it can be concluded that there has been an increase in the number of benchmarks, allowing to compare research results against specific performance metrics independently. However, benchmarks vary regarding the number of classes, spatial size, nomenclature, and class division. In this article, we introduce a new annotated 3D dataset - CENAGIS-ALS Benchmark. We propose a benchmark of highly dense lidar point clouds acquired by Leica CityMapper-2 for the Centre of Warsaw, Poland. The area covers 2 km², and the data has a density of 275 pts/m². The dataset consists of a number of classes that are distinguishable for this type of data. In addition to the basic classes, more specialized classes, important from the perspective of urban space, are also distinguished. Moreover, the division of classes consists of three levels of detail from coarse (e.g., a building) to refined elements (e.g., roofs, chimneys, and other rooftop objects). This benchmark can contribute to geospatial societies, considering the large spatial size of the study area with unified data quality and the higher number of classes with the hierarchical division compared to other benchmarking data.

Lately, more and more laser scanners on UAVs have been launched to the market. In the literature, research can be found in which the point cloud accuracy, completeness as well as reliability of object 3D reconstruction on the point clouds from different laser scanners is described. However, there are also applications which do not require high accuracy, for example, the Digital Terrain Model (DTM) for forestry areas. In this article, the results of the experiment are presented in which DTMs generated for forestry from point clouds from two different UAV laser scanners were compared. The laser scanners that were compared were DJI Zenmuse L1 and Riegl miniVUX-3UAV. The point clouds were post-processed, and classification was carried out. After automatic classification, manual corrections were conducted based on the cross sections through the point cloud. Finally, DTMs and their derivatives were generated and compared. The results showed that the DTMs from UAV laser scanners provided more detailed ground information which enables deeper archaeological analysis.

A total of 2848 denarii from the Nietulisko Małe Hoard, one of the largest hoards of Roman coins found in Poland, were digitised and documented using reflectance transformation imaging, highlighting the potential for this technology in numismatic research.