ISPRS Open Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Benchmarking for practice: Few-shot time-series crop-type classification on the EuroCropsML dataset","authors":"Joana Reuss ,&nbsp;Jan Macdonald ,&nbsp;Simon Becker ,&nbsp;Ekaterina Gikalo ,&nbsp;Konrad Schultka ,&nbsp;Lorenz Richter ,&nbsp;Marco Körner","doi":"10.1016/j.ophoto.2026.100117","DOIUrl":"10.1016/j.ophoto.2026.100117","url":null,"abstract":"<div><div>Accurate crop-type classification from satellite time series is essential for agricultural monitoring. Consequently, various <em>machine learning (ML)</em> algorithms, aiming on enhancing classification performance on data-scarce tasks, have been developed. While previous evaluations demonstrated the effectiveness of these algorithms in certain situations, these studies frequently lacked real-world scenarios. Hence, the performance of the algorithms in challenging practical applications has not yet been profoundly evaluated. To facilitate future research in this domain, we present the first comprehensive benchmark for evaluating supervised and <em>self-supervised learning (SSL)</em> methods for crop-type classification under real-world conditions. This benchmark study relies on the EuroCropsML time-series dataset, which combines farmer-reported crop data with Sentinel-2 satellite observations from Estonia, Latvia, and Portugal. Our findings indicate that variants of <em>Model-Agnostic Meta-Learning (MAML;</em> <span><span>Finn et al., 2017</span></span><em>)</em> achieve slightly higher accuracy compared to supervised transfer learning and SSL. For instance, algorithms belonging to the MAML-family show on average a 31% higher overall accuracy for a 20-shot benchmark task when compared to regular transfer learning. However, compared to simpler transfer learning, the improvement of meta-learning comes at the cost of increased computational demands and training time. Moreover, supervised methods benefit most when pre-trained and fine-tuned on geographically close regions. In addition, while SSL generally lags behind meta-learning, it demonstrates advantages over training from scratch — particularly in capturing fine-grained features essential for real-world crop-type classification — and also surpasses standard transfer learning. This highlights its practical value when labeled pre-training crop data is scarce. Our insights highlight the trade-offs between accuracy and computational demand in selecting supervised machine learning methods for real-world crop-type classification tasks and underscore the difficulties of knowledge transfer across diverse geographic regions. Furthermore, they demonstrate the practical value of SSL approaches when labeled pre-training crop data is scarce. The corresponding code is publicly available at <span><span>https://github.com/jsreu/EurocropsML-Meta-Learning</span><svg><path></path></svg></span> and <span><span>https://github.com/jsreu/EuroCropsML-SSL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"19 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature-based multi-epoch rock slope monitoring using images and terrestrial laser scans","authors":"Lukas Lucks,&nbsp;Christoph Holst","doi":"10.1016/j.ophoto.2026.100120","DOIUrl":"10.1016/j.ophoto.2026.100120","url":null,"abstract":"<div><div>This study presents a comprehensive workflow for feature-based motion tracking of unstable rock slopes, exemplified at Mt. Hochvogel, using terrestrial imagery and terrestrial laser scanning (TLS). The workflow begins with multi-view images and TLS data acquired at different epochs. Features are detected both within each epoch, to reconstruct their 3D positions, and across epochs, to enable consistent motion tracking over time. TLS point clouds are integrated by projecting them into 2D image views, allowing a unified feature-based analysis across data sensors. The capability of this approach for monitoring over multiple acquisitions in the years 2018–2024 is evaluated using three different matching algorithms (SIFT, SuperPoint with LightGlue, and LoFTR). The results demonstrate that both image-based and TLS-based tracking can reliably capture slope movements. The evaluation against reference data captured with a total station shows that the results deviate on average by 1–6 mm in length and 4–7°in direction. Comparing the results from the two different sensors, the results show that image-based tracking generally achieves higher accuracy and more extensive coverage than tracking based on 2D representations derived from TLS point clouds. The analysis on Mt. Hochvogel shows that the main slope movement, characterized by a continuous opening of the large crevice at the summit, continued during the observation period. Apart from minor local movements, no significant acceleration or deceleration of the overall movement was observed. The spatial patterns indicate consistent block displacements of approximately 18–23 mm per year, with local variations in eroded or debris-covered areas. The image data used in this study are published as the HovoPhoto40 dataset (five epochs, 40 images each), providing a publicly available resource with potential for benchmarking photogrammetric monitoring workflows ( <span><span>https://doi.org/10.5281/zenodo.16631844</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"19 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing point cloud semantic segmentation via scalable domain adaptation with LoRA-enabled PointNet++","authors":"Mariona Carós ,&nbsp;Ariadna Just ,&nbsp;Santi Seguí ,&nbsp;Jordi Vitrià","doi":"10.1016/j.ophoto.2026.100119","DOIUrl":"10.1016/j.ophoto.2026.100119","url":null,"abstract":"<div><div>Semantic segmentation of airborne LiDAR point clouds enables a broad range of urban and environmental applications. However, domain shifts between training and operational data, as well as the frequent emergence of new semantic classes, pose significant challenges for deploying deep learning models effectively. In this work, we explore the integration of Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning technique, into the PointNet++ architecture to address these challenges. We evaluate LoRA in two realistic scenarios: domain adaptation and incremental learning with novel classes, using subsets of large-scale LiDAR datasets under constrained labeled data settings. Our experiments show that LoRA outperforms traditional full fine-tuning, achieving notable gains (+3.1 IoU for specific classes and +0.3 mIoU on TerLiDAR, +2.7 mIoU on DALES), while exhibiting greater resistance to catastrophic forgetting and improved generalization, particularly for underrepresented classes. Furthermore, LoRA exceeds baseline accuracy with substantially fewer trainable parameters (73.4% reduction), highlighting its suitability for resource-constrained deployment scenarios. We also present TerLiDAR, a publicly available annotated airborne LiDAR dataset covering 51.4 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> along the Ter River in Catalonia, Spain. It contributes to increasing the diversity of semantic segmentation benchmarks and advancing 3D scene understanding in remote sensing.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"19 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GAST: A graph-augmented spectral–spatial transformer with adaptive gated fusion for small-sample hyperspectral image classification","authors":"Faruk Keskin ,&nbsp;Fesih Keskin ,&nbsp;Gültekin Işık","doi":"10.1016/j.ophoto.2026.100116","DOIUrl":"10.1016/j.ophoto.2026.100116","url":null,"abstract":"<div><div>Accurate hyperspectral image (HSI) classification under scarce labels and class imbalance requires models that couple long-range spectral reasoning with irregular local spatial context. We present GAST, a Graph-Augmented spectral–spatial Transformer with Adaptive Gated Fusion for Small-Sample Hyperspectral Image Classification. GAST pairs a lightweight spectral Transformer with a GATv2-based spatial branch on an 8-neighbor pixel graph, and fuses them via a center-conditioned, channel-wise gating mechanism that uses the center-pixel representation to modulate all tokens in the patch. Unlike conventional static fusion strategies (e.g., concatenation or summation) that assign fixed importance to modalities regardless of image content, this adaptive fusion dynamically modulates the spectral and spatial streams at the pixel level, allowing the model to prioritize spatial texture for complex urban structures while shifting focus to spectral signatures for subtle vegetation classes. Training is further stabilized by an imbalance-aware objective that switches between weighted cross-entropy and focal loss according to a measured class ratio, and by a two-stage Bayesian hyperparameter search that aligns capacity with scene statistics. Across eight public benchmarks under a 5%-label protocol, GAST consistently matches or surpasses recent hybrid graph-Transformer architectures while remaining compact and fast at inference. Ablation studies confirm the complementary roles of both branches and the benefit of gated fusion. The resulting architecture offers a strong accuracy–efficiency trade-off and reliable performance across seeds, making it a practical solution for low-data HSI applications. The code is publicly available at <span><span>https://github.com/fesihkeskin/GAST</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"19 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring tropical forests with light drones: ensuring spatial and temporal consistency in stereophotogrammetric products","authors":"Nicolas Barbier ,&nbsp;Pierre Ploton ,&nbsp;Hadrien Tulet ,&nbsp;Gaëlle Viennois ,&nbsp;Hugo Leblanc ,&nbsp;Benoît Burban ,&nbsp;Maxime Réjou-Méchain ,&nbsp;Philippe Verley ,&nbsp;James Ball ,&nbsp;Denis Feurer ,&nbsp;Grégoire Vincent","doi":"10.1016/j.ophoto.2025.100114","DOIUrl":"10.1016/j.ophoto.2025.100114","url":null,"abstract":"<div><div>Light drones provide a cheap and effective tool to monitor forest canopy, especially in tropical and equatorial contexts, where infrastructure and resources are limiting. In these regions, good quality optical satellite images are rare, yet the stakes are maximal to characterize forest function, dynamics, diversity, and phenology, and more generally the vegetation-climate interplay.</div><div>We describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution. Our target is to allow individual tree-level monitoring over tens to hundreds of hectare scales with consumer grade equipment (i.e., quadcopter with stabilized RGB camera, standard GNSS positioning).</div><div>We demonstrate the increase in spatial accuracy achieved using Time-SIFT and Arosics algorithms, which allow (individually and synergistically) to reduce global and local spatial misalignment between mosaics from several meters to a few centimeters. Time-SIFT provides the advantage of increased robustness in initial image alignment and 3D reconstruction, and hence reduces occasional distortions or data gaps. Using Agisoft's color and white balance corrections combined with the use of vegetation indices provides meaningful quantitative signal despite considerable changes in acquisition conditions.</div><div>In particular, indices that are less sensitive to illumination changes, like the green chromatic coordinate (GCC), allowed evidencing a seasonal signal over four years of monitoring in the evergreen moist forest at Paracou in French Guiana. The signal was decorrelated from obvious geometrical effect (sun height), and provided information on the vegetative stage at tree, species, and stand levels.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"19 ","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards monitoring livestock using satellite imagery: Transferability of object detection and segmentation models in Kenyan rangelands","authors":"Ian A. Ocholla ,&nbsp;Janne Heiskanen ,&nbsp;Faith Karanja ,&nbsp;Mark Boitt ,&nbsp;Petri Pellikka","doi":"10.1016/j.ophoto.2025.100106","DOIUrl":"10.1016/j.ophoto.2025.100106","url":null,"abstract":"<div><div>Over the past four decades, rising demand for livestock products in Africa has led to increased stocking rates resulting in overgrazing and land degradation. As the population is projected to rise, the need for sustainable livestock management is more urgent than ever, yet efforts are hindered by the lack of accurate, up-to-date livestock counts. Recent advances in remote sensing and deep learning have made it possible to count livestock from space. However, the extent to which models trained on aerial imagery can enhance livestock detection in satellite images and across diverse landscapes remains limited. This study assessed the transferability of YOLO, Faster R-CNN, U-Net, and ResNet models for livestock detection across three contrasting landscapes, Choke bushland (Pleiades Neo), Kapiti savanna (WorldView-3), and LUMO open grassland (WorldView-3), using satellite imagery with 0.3 m and 0.4 m spatial resolution. Additionally, we applied a multi-stage transfer learning to evaluate the effectiveness of aerial imagery (0.1 m) trained models in improving livestock detection in satellite imagery. Results indicate that YOLOv5 consistently outperformed other models, achieving F1 scores of 0.55, 0.67, and 0.85 in Choke, Kapiti, and LUMO, respectively, demonstrating robustness across varying land cover types and sensors. Although segmentation models performed moderately on 0.3 m imagery (F1 scores of 0.51 and 0.40 for Choke and LUMO), their performance dropped significantly on the coarser resolution (0.4 m) Kapiti imagery (F1 score of 0.14). In addition, multi-stage transfer learning improved segmentation models recall by 9.8 % in heterogeneous bushland site. Our results highlight that the integration of multi-source imagery and deep learning can help in large scale livestock monitoring, which is crucial in implementing sustainable rangeland management.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"18 ","pages":"Article 100106"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation of precise 3D building models for digital twin projects using multi-source data fusion and integration into virtual tours","authors":"Umut Gunes Sefercik,&nbsp;Ilyas Aydin,&nbsp;Mertcan Nazar","doi":"10.1016/j.ophoto.2025.100108","DOIUrl":"10.1016/j.ophoto.2025.100108","url":null,"abstract":"<div><div>High-quality production of building digital twins (DT) is always a challenging issue. In this study, a methodology is proposed to obtain a precise georeferenced 3D building model with high geometric and spectral quality, which is one of the essential components of a high-quality DT production, through the fusion of UAV and terrestrial photogrammetric data. To better evaluate the performance of the proposed methodology a complex building with glass facades, entrance porches, outdoor stairs, and architectural coverings was chosen. The techniques, used for overcoming the challenging issues about multi-source image orientation, spectral enhancement and precise building model production were presented. In the proposed methodology, distinct from the existing literature studies, photos obtained from different sources were not merged in an image-pool before photogrammetric processing, and geometric and spectral calibrations of aerial and terrestrial photos are completed separately before data fusion. In this manner, individual dense point clouds were both generated based on structure from motion (SfM) and made noise-free with filtering in Bentley ContextCapture software. Precise 3D building model production involved first merging the geo-referenced point clouds, followed by 3D model generation from the fused cloud. The production methodology involved first merging the geo-referenced point clouds, followed by 3D model generation from the fused cloud. The building model was achieved with the geometric accuracy (RMSE) of ≤ ±2 cm by the fusion of ±1.87 cm and ±1.17 cm accuracy UAV and terrestrial photogrammetry dense point clouds, respectively. In addition, an indoor model was generated by capturing 360° panoramic photos of the building and a complete virtual tour was created by merging indoor and outdoor data in the Unity game engine platform.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"18 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circlegrammetry for drone imaging: Evaluating a novel technique for mission planning and 3D mapping","authors":"Mathieu F. Bilodeau ,&nbsp;Travis J. Esau ,&nbsp;Mason T. MacDonald ,&nbsp;Aitazaz A. Farooque","doi":"10.1016/j.ophoto.2025.100111","DOIUrl":"10.1016/j.ophoto.2025.100111","url":null,"abstract":"<div><div>Circlegrammetry is a new drone photogrammetry technique that utilizes circular flight paths. This approach promises higher efficiency for 3D modelling compared to traditional grid-based methods. This study evaluates its performance in a Christmas tree (Balsam fir) field, a complex agricultural environment characterized by intricate vegetation geometry. Experiments were conducted in a 2-ha orchard located in Truro, Nova Scotia, using a DJI Matrice 300 RTK equipped with a high-resolution optical camera. Three Circlegrammetry missions with varying overlaps (25 and 50 %) and flight heights (40 and 60 m) were compared against standard oblique and smart oblique drone missions flown at an flight heights of 60 m. Mission assessments focused on flight efficiency, processing performance and reconstruction accuracy. The point density of the tree canopy, generated from dense point clouds, was also evaluated against different survey methods. Results demonstrated that Circlegrammetry significantly reduced flight times and the number of images required, particularly at lower overlap configurations. For example, Circlegrammetry with a 25 % overlap achieved mission completion in about half the time required for smart oblique methods and in approximately one-third the duration of standard oblique missions. Processing efficiency was similarly favoured by Circlegrammetry (25 % overlap), with notable reductions in processing times. In terms of reconstruction quality, Circlegrammetry produced spatially accurate models with ground-control RMSE values ranging from 1.38 to 1.53 cm. These results were comparable to those of traditional oblique methods, despite not utilizing nadir imagery. However, Circlegrammetry showed limitations in capturing lower canopy details on the tree, with an average point density higher than that of other methods. For example, Circle 25 % performed the worst, with an average point spacing of 15.79 points per millimetre for the lower canopy. In contrast, the standard oblique approach performed the best, with an average point spacing of 11.89 points per millimetre. This suggested some constraints inherent to the inward-facing of the camera and higher oblique-angle flight paths on Cirlegrammetry missions. Overall, Circlegrammetry emerges as a promising method for precision agriculture applications by striking a balance between flight efficiency and reconstruction detail. Circlegrammetry with a 50 % overlap was demonstrated to be a comparable alternative to the smart oblique acquisition method. Future research should focus on optimizing overlap percentages and flight configurations to improve lower canopy coverage further and generalize these findings across diverse agricultural contexts.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"18 ","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global, multi-scale standing deadwood segmentation in centimeter-scale aerial images","authors":"Jakobus Möhring ,&nbsp;Teja Kattenborn ,&nbsp;Miguel D. Mahecha ,&nbsp;Yan Cheng ,&nbsp;Mirela Beloiu Schwenke ,&nbsp;Myriam Cloutier ,&nbsp;Martin Denter ,&nbsp;Julian Frey ,&nbsp;Matthias Gassilloud ,&nbsp;Anna Göritz ,&nbsp;Jan Hempel ,&nbsp;Stéphanie Horion ,&nbsp;Tommaso Jucker ,&nbsp;Samuli Junttila ,&nbsp;Pratima Khatri-Chhetri ,&nbsp;Kirill Korznikov ,&nbsp;Stefan Kruse ,&nbsp;Etienne Laliberté ,&nbsp;Michael Maroschek ,&nbsp;Paul Neumeier ,&nbsp;Clemens Mosig","doi":"10.1016/j.ophoto.2025.100104","DOIUrl":"10.1016/j.ophoto.2025.100104","url":null,"abstract":"<div><div>With tree mortality rates rising across many regions of the world, efficient methods to map dead trees are becoming increasingly important to monitor forest dieback, assess ecological impacts, and guide management strategies. Deep learning-based pattern recognition combined with the high spatial detail of aerial images from drones or airplanes provides an avenue for mapping dead tree crowns or partial canopy dieback, collectively referred to as standing deadwood. However, current methods for mapping standing deadwood are limited to specific biomes or image resolutions. Here, we present a transformer-based semantic segmentation model that generalizes across forest biomes and a wide range of image resolutions (1–28 cm) for mapping both dead tree crowns and partial canopy dieback. Our approach combines a SegFormer-based transformer architecture for image feature extraction and Focal Tversky Loss to mitigate class imbalance. We used a globally distributed crowd-sourced dataset of 434 high-resolution aerial images and manual delineations of standing deadwood of vastly varying quality. The orthophotos span all major forest biomes and cover 10,778 hectares. To further mitigate imbalances across biomes, resolutions, deadwood occurrence, and image sources, we developed a four-dimensional sampling scheme that ensures balanced representation during training. The models were trained and evaluated using heterogeneous crowd-sourced data, which, as expected, negatively affects the F1-scores. A visual inspection on independent data highlights the very precise quality of the segmentation. Our analysis revealed resolution-dependent performance variations across biomes, suggesting a relationship between optimal mapping resolution and biome-specific characteristics. We make both our model and a machine-learning-ready dataset publicly available on <span><span>deadtrees.earth</span><svg><path></path></svg></span> to support future research in tree mortality mapping.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"18 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145418262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomalous glaciers response to climate variability in the Karakoram region","authors":"Insha Batool ,&nbsp;Arshad Ashraf ,&nbsp;Muhammad Fahim Khokhar","doi":"10.1016/j.ophoto.2025.100105","DOIUrl":"10.1016/j.ophoto.2025.100105","url":null,"abstract":"<div><div>Karakoram glaciers exhibit steady mass or expansion in the central and western Karakoram, contrasting with the retreat observed in the eastern Karakoram and respond differently to climatic conditions compared to glaciers in the world<strong>—</strong>a phenomenon termed as the Karakoram Anomaly. The absence of long-term ground-based monitoring of climatic variables and glaciers observations in addition to the region's complex terrain, remote location, and harsh climate pose a serious challenge to find a precise explanation for anomalous glaciers behavior and their response to ongoing climate variability. This study compares a high-resolution (10 m) geodetic glaciers data set from 1991 to 2022 with climate variables to assess the glaciers mass balance condition across different elevations in the Hunza and Shigar basins and to examine their relationship with climatic drivers. We observe that glaciers maintain a stable mass balance regardless of elevation. Above 4500 m above sea level, glaciers exhibit surges under the unique climate warming of the twenty first century with slight reduction in snowfall—a phenomenon we refer to as the Karakoram Climate Response Anomaly (KCRA). We find that the unique mountainous land and a predominantly north-facing aspect are the main cause of glaciers stability despite prevailing warming climate signatures in the Karakoram range. However, future projections based on CMIP6 ensemble scenarios indicate a challenging future for glaciers sustainability, with rising temperatures and declining precipitation, particularly in the western Karakoram. These findings underscore the critical need for continuous field observations of glaciers and climate conditions to better understand and predict glacier responses to evolving climate conditions in Karakoram.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"18 ","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}