ISPRS Open Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Integrating multi-user digitising actions for mapping gully outlines using a combined approach of Kalman filtering and machine learning","authors":"Miguel Vallejo, K. Anders, O. Ajayi, Olaf Bubenzer, B. Höfle","doi":"10.1016/j.ophoto.2024.100059","DOIUrl":"https://doi.org/10.1016/j.ophoto.2024.100059","url":null,"abstract":"","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"83 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815198","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":"Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment","authors":"Kyriaki Mouzakidou,&nbsp;Aurélien Brun,&nbsp;Davide A. Cucci,&nbsp;Jan Skaloud","doi":"10.1016/j.ophoto.2023.100057","DOIUrl":"10.1016/j.ophoto.2023.100057","url":null,"abstract":"<div><p><em>Tightly-coupled</em> 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 employ direct sensor orientation for the spatial interpretation of laser vectors. In this study, previously reported preliminary results are extended to assess the gain in accuracy of sensor orientation through leveraging all available spatio-temporal constraints in a dynamic network i) with a commercial IMU for drones and ii) with simultaneous processing of raw-observations of several low-quality IMUs. Additionally, we evaluate the influence of different types of spatial constraints (image 2D and point-cloud 3D tie-points) and flight geometries (with and without a cross flight line). We present the newly implemented estimation of confidence levels and compare those with the observed residual errors. The empirical evidence demonstrates that the use of spatial constraints increases the attitude accuracy of the derived trajectory by a factor of 2–3, both for the commercial and low-quality IMUs, while at the same time reducing the dispersion of geo-referencing errors, resulting in a considerably more precise and self-coherent geo-referenced point-cloud. We further demonstrate that the use of image constraints (additionally to lidar constraints) stabilizes the in-flight lidar boresight estimation by a factor of 3–10, establishing the feasibility of such estimation even in the absence of special calibration patterns or calibration targets.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"12 ","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000285/pdfft?md5=0f7ab041b690c142ba3b35d6019ecf11&pid=1-s2.0-S2667393223000285-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139632413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting the Past: A comparative study for semantic segmentation of historical images of Adelaide Island using U-nets","authors":"Felix Dahle,&nbsp;Roderik Lindenbergh,&nbsp;Bert Wouters","doi":"10.1016/j.ophoto.2023.100056","DOIUrl":"https://doi.org/10.1016/j.ophoto.2023.100056","url":null,"abstract":"<div><p>The TriMetrogon Aerial (TMA) archive is an archive of historical images of Antarctica taken by the US Navy between 1940 and 2000 with analogue cameras. The analysis of such historic data can give a view of Antarctica's glaciers predating modern satellite imagery and provide unique insights into the long-term impact of changing climate conditions with essential validation data for climate modelling. However, the lack of semantic information for these images presents a challenge for large-scale computer-driven analysis.</p><p>Such information can be added to the data using semantic segmentation, but traditional algorithms fail on these scanned historical grayscale images, due to varying image quality, lack of colour information and artefacts in the images. To address this, we present a deep-learning-based U-net workflow. Our approach includes creating training data by pre-processing and labelling the raw images. Furthermore, different versions of the U-net are trained to optimize its hyperparameters and augmentation methods. With the optimal hyper-parameters and augmentation methods, a final model has been trained for a use-case to segment 118 images covering Adelaide Island.</p><p>We tested our approach by segmenting challenging historical images using a U-net model with just 80 training images, achieving an accuracy of 73% for 20 validation images. While no test data is available for our use case, a visual examination of the segmented images shows that our method performs effectively.</p><p>The comparison of the hyper-parameters and augmentation methods provides directions for training other U-net-based models so that the presented workflow can be used to segment other archives with historical imagery. Additionally, the labelled training data and the segmented images of the test are publicly available at <span>https://github.com/fdahle/antarctic_segmentation</span><svg><path></path></svg>.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"11 ","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000273/pdfft?md5=d102ce83a2ff8228dd333428f7d3bf8e&pid=1-s2.0-S2667393223000273-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139107227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principled bundle block adjustment with multi-head cameras","authors":"Eleonora Maset ,&nbsp;Luca Magri ,&nbsp;Andrea Fusiello","doi":"10.1016/j.ophoto.2023.100051","DOIUrl":"https://doi.org/10.1016/j.ophoto.2023.100051","url":null,"abstract":"<div><p>This paper examines the effects of implementing relative orientation constraints on bundle adjustment, as well as provides a full derivation of the Jacobian matrix for such an adjustment, that can be used to facilitate other implementations of bundle adjustment with constrained cameras. We present empirical evidence demonstrating improved accuracy and reduced computational load when these constraints are imposed.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"11 ","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000224/pdfft?md5=104b2b21116c9955ace52700652a666b&pid=1-s2.0-S2667393223000224-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139111422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistically assessing vertical change on a sandy beach from permanent laser scanning time series","authors":"Mieke Kuschnerus ,&nbsp;Roderik Lindenbergh ,&nbsp;Sander Vos ,&nbsp;Ramon Hanssen","doi":"10.1016/j.ophoto.2023.100055","DOIUrl":"https://doi.org/10.1016/j.ophoto.2023.100055","url":null,"abstract":"<div><p>In the view of climate change, understanding and managing effects on coastal areas and adjacent cities is essential. Permanent Laser Scanning (PLS) is a successful technique to not only observe notably sandy coasts incidentally or once every year, but (nearly) continuously over extended periods of time. The collected point cloud observations form a 4D point cloud data set representing the evolution of the coast provide the opportunity to assess change processes at high level of detail. For an exemplary location in Noordwijk, The Netherlands, three years of hourly point clouds were acquired on a 1 km long section of a typical Dutch urban sandy beach. Often, the so-called level of detection is used to assess point cloud differences from two epochs. To explicitly incorporate the temporal dimension of the height estimates from the point cloud data set, we revisit statistical testing theory. We apply multiple hypothesis testing on elevation time series in order to identify different coastal processes, like aeolian sand transport or bulldozer works. We then estimate the minimal detectable bias for different alternative hypotheses, to quantify the minimal elevation change that can be estimated from the PLS observations over a certain period of time. Additionally, we analyse potential error sources and influences on the elevation estimations and provide orders of magnitudes and possible ways to deal with them. Finally we conclude that elevation time series from a long term PLS data set are a suitable input to identify aeolian sand transport with the help of multiple hypothesis testing. In our example case, slopes of 0.032 m/day and sudden changes of 0.031 m can be identified with statistical power of 80% and with 95% significance in 24-h time series on the upper beach. In the intertidal area the presented method allows to classify daily elevation time series over one month according to the dominating model (sudden change or linear trend) in either eroding or accreting behaviour.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"11 ","pages":"Article 100055"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000261/pdfft?md5=2b715eedb9e8c262b3b531332998a270&pid=1-s2.0-S2667393223000261-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139107208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICESat-2 noise filtering using a point cloud neural network","authors":"Mariya Velikova,&nbsp;Juan Fernandez-Diaz,&nbsp;Craig Glennie","doi":"10.1016/j.ophoto.2023.100053","DOIUrl":"10.1016/j.ophoto.2023.100053","url":null,"abstract":"<div><p>The ATLAS sensor onboard the ICESat-2 satellite is a photon-counting lidar (PCL) with a primary mission to map Earth's ice sheets. A secondary goal of the mission is to provide vegetation and terrain elevations, which are essential for calculating the planet's biomass carbon reserves. A drawback of ATLAS is that the sensor does not provide reliable terrain height estimates in dense, high-closure forests because only a few photons reach the ground through the canopy and return to the detector. This low penetration translates into lower accuracy for the resultant terrain model. Tropical forest measurements with ATLAS have an additional problem estimating top of canopy because of frequent atmospheric phenomena such as fog and low clouds that can be misinterpreted as top of the canopy. To alleviate these issues, we propose using a ConvPoint neural network for 3D point clouds and high-density airborne lidar as training data to classify vegetation and terrain returns from ATLAS. The semantic segmentation network provides excellent results and could be used in parallel with the current ATL08 noise filtering algorithms, especially in areas with dense vegetation. We use high-density airborne lidar data acquired along ICESat-2 transects in Central American forests as a ground reference for training the neural network to distinguish between noise photons and photons lying between the terrain and the top of the canopy. Each photon event receives a label (noise or signal) in the test phase, providing automated noise-filtering of the ATL03 data. The terrain and top of canopy elevations are subsequently aggregated in 100 m segments using a series of iterative smoothing filters. We demonstrate improved estimates for both terrain and top of canopy elevations compared to the ATL08 100 m segment estimates. The neural network (NN) noise filtering reliably eliminated outlier top of canopy estimates caused by low clouds, and aggregated root mean square error (RMSE) decreased from 7.7 m for ATL08 to 3.7 m for NN prediction (18 test profiles aggregated). For terrain elevations, RMSE decreased from 5.2 m for ATL08 to 3.3 m for the NN prediction, compared to airborne lidar reference profiles.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"11 ","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000248/pdfft?md5=90f41b323182f63f9bad036a38f7b9ea&pid=1-s2.0-S2667393223000248-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138621053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated data-driven approach to monitor and estimate plant-scale growth using UAV","authors":"Philippe Vigneault ,&nbsp;Joël Lafond-Lapalme ,&nbsp;Arianne Deshaies ,&nbsp;Kosal Khun ,&nbsp;Samuel de la Sablonnière ,&nbsp;Martin Filion ,&nbsp;Louis Longchamps ,&nbsp;Benjamin Mimee","doi":"10.1016/j.ophoto.2023.100052","DOIUrl":"https://doi.org/10.1016/j.ophoto.2023.100052","url":null,"abstract":"<div><p>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. New findings support that it is possible to parametrize an entire crop growth cycle under different conditions by accumulating sufficient data over time and using logistic growth models to highlight growth patterns. A step forward would be to model crop growth cycle at the plant-level in order to anticipate the optimal harvest dates in each plot or quickly identify growth problematics. Individual plant monitoring can be achieved by combining high spatial resolution images with accurate segmentation algorithms. The main objective of the study was therefore to develop and validate an integrated pipeline based on multidimensional data to extract predictive growth metrics for crop monitoring at the plant-level under various field conditions. The plant growth monitoring workflow was based on a three-step design ultimately leading to decision-making and reporting. Lettuce (<em>Lactuca sativa</em> L.) was chosen as a model plant due to its simple geometry, rapid growth and simple cultivation method. Treatments were composed of contrasting cover crops. Overall, correlation analysis showed that UAV-derived morphological metrics are reliable proxies for harvested biomass throughout the growing season, especially in later stages (Spearman's ρ &gt; 0.9) and can be used as growth indicators. Therefore, Logistic Growth Curves (LGCs) were fitted to Crop Object Area (COA) values for each individual lettuce, using data up to 26 (generating G₂₆ LGCs), 30 (G₃₀) and 37 (G₃₇) Days After Transplant (DAT). To assess the quality of their projections, G₂₆ and G₃₀ were compared to the reference LGC G₃₇. The results indicated that Mean Absolute Percentage Error (MAPE) of projected COA was 9.6% and 6.8% for G₂₆ and G₃₀ respectively. Overall, the LGC parameters were close to the reference and highly correlated with the harvested biomass. The study also demonstrated the potential of having very good insight on plant maturity level by modeling the LGC 13 days before harvest. Furthermore, a dashboard was proposed to monitor current and projected maturity level, highlighting areas for further investigation. This novel integrated pipeline has the potential to become a valuable tool for research, on-farm decision making, and field interventions by providing data on plant biomass, maturity, and growth stages under different conditions, used as crop growth indicators.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"11 ","pages":"Article 100052"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000236/pdfft?md5=d5a0738ff9505d3deb1b9b7a25a6d55e&pid=1-s2.0-S2667393223000236-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138549946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How suitable are vegetation indices for estimating the (R)USLE C-factor for croplands? A case study from Southeast Brazil","authors":"Filipe Castro Felix ,&nbsp;Bernardo M. Cândido ,&nbsp;Jener F.L. de Moraes","doi":"10.1016/j.ophoto.2023.100050","DOIUrl":"https://doi.org/10.1016/j.ophoto.2023.100050","url":null,"abstract":"<div><p>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 rely on vegetation-index-based regressions to directly estimate the cover and management factor (C-factor) of the USLE from satellite images. Since this approach is based on a single or very few images, it does not characterize the seasonality of the crop cover or reflect the seasonality of the rainfall erosivity. Here, we evaluated five vegetation indices (NDVI, NDRE, SFDVI, ViGREEN, and MGRVI) in predicting SLRs and the C-factor for a sugarcane plot in Southeast Brazil. We used Sentinel-2 images and orthomosaics obtained by UAV surveys performed at the middle of each phenological stage. We compared the estimates of the C-factor based on the SLRs and rainfall erosivity against direct regressions from the literature. Our results confirmed the expected poor correlation between the C-factor and the vegetation indices. On the other hand, using the proposed vegetation indices proved to be a reliable alternative to predict the SLR in sugarcane areas, especially the NDVI, the NDRE, and MGRVI. In particular, the MGRVI accurately predicted the SLR and classified the UAV-derived orthomosaics.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"10 ","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000212/pdfft?md5=50776dce02cfabfb6d46015c263e3d0e&pid=1-s2.0-S2667393223000212-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134657054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral Profile Partial Least-Squares (SP-PLS): Local multivariate pansharpening on spectral profiles","authors":"Tuomas Sihvonen,&nbsp;Zina-Sabrina Duma,&nbsp;Heikki Haario,&nbsp;Satu-Pia Reinikainen","doi":"10.1016/j.ophoto.2023.100049","DOIUrl":"https://doi.org/10.1016/j.ophoto.2023.100049","url":null,"abstract":"<div><p>The compatibility of multispectral (MS) pansharpening algorithms with hyperspectral (HS) data is limited. With the recent development in HS satellites, there is a need for methods that can provide high spatial and spectral fidelity in both HS and MS scenarios.</p><p>The present article presents a fast pansharpening method, based on the division of similar hyperspectral data in spectral subgroups using k-means clustering and Spectral Angle Mapper (SAM) profiling. Local Partial Least-Square (PLS) models are calibrated for each spectral subgroup against the respective pixels of the panchromatic image. The models are inverted to retrieve high-resolution pansharpened images. The method is tested against different methods that are able to handle both MS and HS pansharpening and assessed using reduced- and full-resolution evaluation methodologies. Based on a statistical multivariate approach, the proposed method is able to render uncertainty maps for spectral or spatial fidelity - functionality not reported in any other pansharpening study.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"10 ","pages":"Article 100049"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667393223000200/pdfft?md5=8601d34da365ecdb3113dcf7bf967e02&pid=1-s2.0-S2667393223000200-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92073808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification","authors":"Sylvia Hochstuhl ,&nbsp;Niklas Pfeffer ,&nbsp;Antje Thiele ,&nbsp;Stefan Hinz ,&nbsp;Joel Amao-Oliva ,&nbsp;Rolf Scheiber ,&nbsp;Andreas Reigber ,&nbsp;Holger Dirks","doi":"10.1016/j.ophoto.2023.100047","DOIUrl":"https://doi.org/10.1016/j.ophoto.2023.100047","url":null,"abstract":"<div><p>This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: <span>https://doi.org/10.35097/1700</span><svg><path></path></svg>.</p></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"10 ","pages":"Article 100047"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49739522","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}