Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 14 references
TL;DR
The upgraded version of the geometric correction module of the STORM processing chain can automatically orthorectify images from the NEMO-HD small satellite, which, like other small satellites, in principle has a lower signal-to-noise ratio (SNR) and higher radiometric variability.
Abstract
Abstract. In recent decades, many different satellites for Earth observation have been launched. They produce large amounts of data that, if properly preprocessed, can be used in many applications. A rapidly growing portion of these data comes from small satellites, which remain underused by scientists and entrepreneurs. However, greater utilisation can be ensured by producing images with positional accuracy of at least two pixels, which is necessary for their reliable use. This paper presents the upgraded version of the geometric correction module of the STORM processing chain. The module can automatically orthorectify images from the NEMO-HD small satellite, which, like other small satellites, in principle has a lower signal-to-noise ratio (SNR) and higher radiometric variability. It automatically extracts ground control points (GCPs) by matching freely available reference vector roads and reference images to roads extracted from the satellite image using a deep learning method trained on PlanetScope and NEMO-HD imagery. The performance of the geometric correction module was evaluated using three images acquired over Slovenia. The road extraction method can achieve an F1-score of approximately 60%. The tests demonstrated that automatic GCP extraction based on roads detected by a deep learning method is a viable approach for achieving geometric model accuracies of two pixels or less at independent check points when using small satellite imagery.
Accurate satellite tracking requires up-to-date Two-Line Elements (TLEs), as outdated data can lead to significant positioning errors. While ground-based optical telescopes are highly accessible, generating TLEs from their data is complicated by the fundamental lack of direct range measurements. This paper presents an automated end-to-end pipeline designed to overcome this limitation by proposing a robust method to estimate the range from prior TLE. The pipeline can then generate the updated TLEs by calculating new satellite state vectors using the estimated range. The pipeline consists of star and satellite detection, astrometric calibration, and orbit determination. For star and satellite detection, the key component of the pipeline is a robust deep learning-based detection model. To achieve this, we benchmarked models such as Deformable DETR, RF-DETR, and YOLOv12 against traditional image processing methods with 3105 images in FITS (Flexible Image Transport System) format. RF-DETR yields the highest F1 score (0.93) and precision (0.97) at an 8-pixel threshold. The detected star coordinates resulting from using RF-DETR, the best-performing model, were fed into Astrometry.net for precise astrometric calibration to determine the satellite celestial coordinates. The range required for orbit determination was estimated by extracting the prior TLE and propagating it to the observation epoch via SGP4. The satellite state vectors were then calculated using TLE-constrained orbit determination approach using the estimated range, followed by an inverse SGP4 optimization to recover the mean orbital elements. The generated TLEs were validated against public TLEs from Space-Track.org. To evaluate this pipeline, updated TLEs were generated specifically for medium Earth orbit (MEO) and geostationary Earth orbit (GEO) targets. The results demonstrate that the proposed method yields high accuracy for GEO satellites, achieving a mean motion difference of 0.0030 rev/day and a 24-hour ground-track position error of 0.83 degrees. In comparison, MEO satellites achieve a mean motion difference of 0.0126 rev/day and an error of 5.27 degrees. These results suggest that the proposed pipeline provides a robust foundation for automated orbit determination with clear potential for further refinement.
Kamin Kanchanapradit, K. Noysena, R. Lipikorn· IEEE Access· 0 citations
Street View Images (SVI) are high-resolution, geo-referenced panoramas that capture real-world environments. Integration of Artificial Intelligence (AI) with SVI enables automated analysis for a range of urban applications including object detection, semantic segmentation, text recognition, scene understanding, and socioeconomic prediction. More than 25 recent AI based SVI studies covering applications in crime prediction, building attribute classification, sidewalk inventory, land price estimation, and environmental monitoring were screened for the survey. Across domains, deep learning architectures such as ResNet, ConvNeXt, and Vision Transformers consistently outperformed traditional machine learning models, with reported accuracies up to 94% for classification and R2 values between 0.62–0.83 for prediction tasks. SVI augmented with other data sources like satellite imagery and Global Information System (GIS), enhanced the model performance and contextual understanding. The Findings highlight the dominance of convolutional and transformer-based networks, emerging interest in graph neural networks, and the need for generalized models and diverse datasets to advance SVI research.
Ranjani A, J. C, V. V et al.· 2026 7th International Confe...· 0 citations
Abstract. Road extraction from small satellite imagery is challenging because raw images often suffer from low signal-to-noise ratio (SNR), high radiometric variability, and reduced sharpness. In this work, we investigate whether noise and blur data augmentation during pretraining can improve robustness in such conditions. We use a two-stage transfer-learning framework in which a U-Net with a ResNet-50 encoder is pretrained on PlanetScope RGB imagery and fine-tuned on NEMO-HD imagery. During pretraining, we evaluate Gaussian, ISO-like, and Perlin noise, as well as Gaussian and motion blur, each at three severity levels. On the internal held-out test split, augmentation effects were modest, with the best strict IoU improving from 26.7% for the geometric-only baseline to 27.2%. However, evaluation on external full-scene NEMO-HD images showed clearer benefits. Augmentation-based models consistently improved road detection in raw imagery, mainly by increasing completeness and recall, while there was little or no systematic benefit in stacked imagery. No clear trend was observed across augmentation severity levels, indicating that performance depended more on scene conditions than on perturbation strength. The results show that augmentation is most useful for single-acquisition small satellite imagery, where it improves robustness to lower image quality.
Nina Krašovec, Aleš Marsetič· The International Archives o...· 0 citations
Abstract. Satellite imagery offers a distinct advantage in Earth observation by providing expansive coverage and enabling the monitoring of inaccessible regions without physical on-site intervention, serving as a significantly more cost-effective and scalable alternative to traditional aerial or ground-based surveys. The task of 3D reconstruction from multi-view satellite images has therefore been a pivotal point of research at the intersection of photogrammetry and remote sensing. Recently, novel-view synthesis techniques such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have accelerated the accuracy and speed of topographic modeling. Among these, Earth Observation Gaussian Splatting (EOGS) has emerged as a state-of-the-art approach by adapting 3DGS to handle the unique geometric and radiometric characteristics of satellite data, including Rational Polynomial Coefficients (RPCs) and varying solar conditions. However, the standard EOGS pipeline relies on stochastic initialization, where Gaussians are distributed uniformly within a volumetric bounding box, leading to high computational overhead and dependency on aggressive pruning that can inadvertently remove critical geometric features, particularly in areas with complex urban structures. To address these limitations, we propose Bundle-Adjusted Initialization for Earth Observation Gaussian Splatting, which leverages sparse point clouds from bundle adjustment as geometric priors for Gaussian initialization. Combined with an adaptive densification strategy, our method achieves faster convergence and improved DSM accuracy on the DFC2019 dataset compared to the EOGS baseline.
Jiyong Kim, Shuang Song, Rongjun Qin· The International Archives o...· 0 citations
This paper examines the problem of land cover classification in densely populated urban environments using ultra-high-resolution Earth observation images. The aim of the study is to develop and validate a neural network algorithm for a high-precision semantic segmentation of urban green spaces using multimodal data. An improved U-Net convolutional neural network architecture, modified for the use with a 7-channel input tensor (RGB, NIR, RedEdge, DSM, and NDVI), is proposed. The approach is based on the Early Fusion strategy, which combines spectral measurements with promising structural characteristics (digital surface model, DSM). Focal Loss is used to overcome the class imbalance. The proposed model reliably separated spectrally identical layers (grass and trees) and eliminated false positives on green anthropogenic objects. The final Mean IoU was 0.725. The recall for detecting forested areas reached 0.93, and for the complex minority class “Shrubs” it reached 0.77. The experiment on an independent test site confirmed the model’s high generalizability (F1-score 0.93). The integration of seven data channels and the use of a modified U-Net are fully justified for the tasks of accurate calculating forest areas and environmental monitoring in the Smart City concept.
A. A. Intyakova, S. Ablameyko· Proceedings of the National...· 0 citations
In the present generation of increasing geospatial data, accurate and automated extraction of building footprints from high-resolution aerial and satellite imagery has become crucial for various applications such as urban planning, infrastructure development, disaster management, and GIS database maintenance, as manual tracing is time-consuming and unstable for large-scale mapping. This study compares conventional image processing techniques such as thresholding, edge detection, morphological operations through a machine learning approach using Random Forest (RF), and deep learning-based semantic segmentation models, namely U-Net and DeepLabV3+, along with the Segment Anything Model (SAM) using a pre-trained prompt-based setup. All methods are tested on the same set of data, and a standardized data preprocessing is performed for fair comparison. The overall results indicate that the application of DeepLabV3+ is best, with an IoU of 82% and an F1 score of 90%. U-Net achieves second high IoU and F1 scores of 74% and 84% respectively, while Random Forest shows a high IoU of 60% and an F1-score of 72%. SAM has the lowest scores with an IoU of 50% and an F1 score of 51%.