PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science最新文献

{"title":"Transformer models for Land Cover Classification with Satellite Image Time Series","authors":"Mirjana Voelsen, Franz Rottensteiner, Christian Heipke","doi":"10.1007/s41064-024-00299-7","DOIUrl":"https://doi.org/10.1007/s41064-024-00299-7","url":null,"abstract":"<p>In this paper we address the task of pixel-wise land cover (LC) classification using satellite image time series (SITS). For that purpose, we use a supervised deep learning model and focus on combining spatial and temporal features. Our method is based on the Swin Transformer and captures global temporal features by using self-attention and local spatial features by convolutions. We extend the architecture to receive multi-temporal input to generate one output label map for every input image. In our experiments we focus on the application of pixel-wise LC classification from Sentinel‑2 SITS over the whole area of Lower Saxony (Germany). The experiments with our new model show that by using convolutions for spatial feature extraction or a temporal weighting module in the skip connections the performance improves and is more stable. The combined usage of both adaptations results in the overall best performance although this improvement is only minimal. Compared to a fully convolutional neural network without any self-attention layers our model improves the results by 2.1% in the mean F1-Score on a corrected test dataset. Additionally, we investigate different types of temporal position encoding, which do not have a significant impact on the performance.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"23 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions","authors":"Mohammed Eunus Ali, Muhammad Aamir Cheema, Tanzima Hashem, Anwaar Ulhaq, Muhammad Ali Babar","doi":"10.1007/s41064-024-00301-2","DOIUrl":"https://doi.org/10.1007/s41064-024-00301-2","url":null,"abstract":"<p>A <i>Digital Twin (DT)</i> is a virtual replica of a physical object or system, created to monitor, analyze, and optimize its behavior and characteristics. A <i>Spatial Digital Twin (SDT)</i> is a specific type of digital twin that emphasizes the geospatial aspects of the physical entity, incorporating precise location and dimensional attributes for a comprehensive understanding of its spatial environment. With the recent advancement in spatial technologies and breakthroughs in other computing technologies such as Artificial Intelligence (AI) and Machine Learning (ML), the SDTs market is expected to rise to 25 billion, covering a wide range of applications. The majority of existing research focuses on DTs and often fails to address the necessary spatial technologies essential for constructing SDTs. The current body of research on SDTs primarily concentrates on analyzing their potential impact and opportunities within various application domains. As building an SDT is a complex process and requires a variety of spatial computing technologies, it is not straightforward for practitioners and researchers of this multi-disciplinary domain to grasp the underlying details of enabling technologies of the SDT. In this paper, we are the first to systematically analyze different spatial technologies relevant to building an SDT in a layered approach (starting from data acquisition to visualization). More specifically, we present the <i>tech stack of SDTs</i> into five distinct layers of technologies: (i) data acquisition and processing; (ii) data integration, cataloging, and metadata management; (iii) data modeling, database management &amp; big data analytics systems; (iv) Geographic Information System (GIS) software, maps, &amp; APIs; and (v) key functional components such as visualizing, querying, mining, simulation, and prediction. Moreover, we discuss how modern technologies such as AI/ML, blockchains, and cloud computing can be effectively utilized in enabling and enhancing SDTs. Finally, we identify a number of research challenges and opportunities in SDTs. This work serves as an important resource for SDT researchers and practitioners as it explicitly distinguishes SDTs from traditional DTs, identifies unique applications, outlines the essential technological components of SDTs, and presents a vision for their future development along with the challenges that lie ahead.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Treating Tropospheric Phase Delay in Large-scale Sentinel-1 Stacks to Analyze Land Subsidence","authors":"Mahmud Haghshenas Haghighi, Mahdi Motagh","doi":"10.1007/s41064-024-00304-z","DOIUrl":"https://doi.org/10.1007/s41064-024-00304-z","url":null,"abstract":"<p>Variations in the tropospheric phase delay pose a primary challenge to achieving precise displacement measurements in Interferometric Synthetic Aperture Radar (InSAR) analysis. This study presents a cluster-based empirical tropospheric phase correction approach to analyze land subsidence rates from large-scale Sentinel‑1 data stacks. Our method identifies the optimum number of clusters in individual interferograms for K‑means clustering, and segments extensive interferograms into areas with consistent tropospheric phase delay behaviors. It then performs tropospheric phase correction based on empirical topography-phase correlation, addressing stratified and broad-scale tropospheric phase delays. Applied to a six-year data stack along a 1000-km track in Iran, we demonstrate that this approach enhances interferogram quality by reducing the standard deviation by 50% and lowering the semivariance of the interferograms to 20 cm² at distances up to 800 km in 97% of the interferograms. Additionally, the corrected time series of deformation shows a 40% reduction in the root mean square of residuals at the most severely deformed points. By analyzing the corrected interferograms, we show that our method improves the efficiency of country-scale InSAR surveys to detect and quantify present-day land subsidence in Iran, which is essential for groundwater management and sustainable water resource planning.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"55 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cooperative Image Orientation with Dynamic Objects","authors":"Philipp Trusheim, Max Mehltretter, Franz Rottensteiner, Christian Heipke","doi":"10.1007/s41064-024-00296-w","DOIUrl":"https://doi.org/10.1007/s41064-024-00296-w","url":null,"abstract":"<p>Using images to supplement classical navigation solutions purely based on global navigation satellite systems (GNSSs) has the potential to overcome problems in densely built-up areas. These approaches usually assume a static environment; however, this assumption is not necessarily valid in urban areas. Therefore, many approaches delete information stemming from moving objects in a first processing step, but this results in information being lost. In this paper, we present an approach that detects and models so-called dynamic objects based on image sequences and includes these object models into a bundle adjustment. We distinguish dynamic objects that provide information about their position to others (cooperating objects) and those that do not (non-cooperating objects). Dynamic objects that observe the environment with the help of sensors in order to determine their position are called observing objects. In the experiments discussed here, the observing object is equipped with a stereo camera and a GNSS receiver. We show that cooperating objects can have a positive effect on the exterior orientation of the observing object after the bundle adjustment, both in terms of precision and accuracy. However, we found that introducing non-cooperating objects did not result in further improvements, probably because in our case the photogrammetric block was already stable without them due to the large number and good distribution of static tie points.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local Evaluation of Large-scale Remote Sensing Machine Learning-generated Building and Road Dataset: The Case of Rwanda","authors":"Emmanuel Nyandwi, Markus Gerke, Pedro Achanccaray","doi":"10.1007/s41064-024-00297-9","DOIUrl":"https://doi.org/10.1007/s41064-024-00297-9","url":null,"abstract":"&lt;p&gt;Accurate and up-to-date building and road data are crucial for informed spatial planning. In developing regions in particular, major challenges arise due to the limited availability of these data, primarily as a result of the inherent inefficiency of traditional field-based surveys and manual data generation methods. Importantly, this limitation has prompted the exploration of alternative solutions, including the use of remote sensing machine learning-generated (RSML) datasets. Within the field of RSML datasets, a plethora of models have been proposed. However, these methods, evaluated in a research setting, may not translate perfectly to massive real-world applications, attributable to potential inaccuracies in unknown geographic spaces. The scepticism surrounding the usefulness of datasets generated by global models, owing to unguaranteed local accuracy, appears to be particularly concerning. As a consequence, rigorous evaluations of these datasets in local scenarios are essential for gaining insights into their usability. To address this concern, this study investigates the local accuracy of large RSML datasets. For this evaluation, we employed a dataset generated using models pre-trained on a variety of samples drawn from across the world and accessible from public repositories of open benchmark datasets. Subsequently, these models were fine-tuned with a limited set of local samples specific to Rwanda. In addition, the evaluation included Microsoft’s and Google’s global datasets. Using ResNet and Mask R‑CNN, we explored the performance variations of different building detection approaches: bottom-up, end-to-end, and their combination. For road extraction, we explored the approach of training multiple models on subsets representing different road types. Our testing dataset was carefully designed to be diverse, incorporating both easy and challenging scenes. It includes areas purposefully chosen for their high level of clutter, making it difficult to detect structures like buildings. This inclusion of complex scenarios alongside simpler ones allows us to thoroughly assess the robustness of DL-based detection models for handling diverse real-world conditions. In addition, buildings were evaluated using a polygon-wise comparison, while roads were assessed using network length-derived metrics.&lt;/p&gt;&lt;p&gt;Our results showed a precision (P) of around 75% and a recall (R) of around 60% for the locally fine-tuned building model. This performance was achieved in three out of six testing sites and is considered the lowest limit needed for practical utility of RSML datasets, according to the literature. In contrast, comparable results were obtained in only one out of six sites for the Google and Microsoft datasets. Our locally fine-tuned road model achieved moderate success, meeting the minimum usability threshold in four out of six sites. In contrast, the Microsoft dataset performed well on all sites. In summary, our findings suggest improved performance ","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"9 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive Mixed Reality Methods for Visualization of Underground Utilities","authors":"Mohamed Zahlan Abdul Muthalif, Davood Shojaei, Kourosh Khoshelham","doi":"10.1007/s41064-024-00295-x","DOIUrl":"https://doi.org/10.1007/s41064-024-00295-x","url":null,"abstract":"<p>This research aims to overcome the difficulties associated with visualizing underground utilities by proposing six interactive visualization methods that utilize Mixed Reality (MR) technology. By leveraging MR technology, which enables the seamless integration of virtual and real-world content, a more immersive and authentic experience is possible. The study evaluates the proposed visualization methods based on scene complexity, parallax effect, real-world occlusion, depth perception, and overall effectiveness, aiming to identify the most effective methods for addressing visual perceptual challenges in the context of underground utilities. The findings suggest that certain MR visualization methods are more effective than others in mitigating the challenges of visualizing underground utilities. The research highlights the potential of these methods, and feedback from industry professionals suggests that each method can be valuable in specific contexts.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"7 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-based Bathymetry Supported by Extracted Coastlines","authors":"Hakan Uzakara, Nusret Demir, Serkan Karakış","doi":"10.1007/s41064-024-00298-8","DOIUrl":"https://doi.org/10.1007/s41064-024-00298-8","url":null,"abstract":"<p>Bathymetry is the measurement of ocean depths using a variety of techniques. Available techniques include sonar systems, light detection and ranging (LIDAR), and remote sensing systems. Acoustic systems, also known as LIDAR, are inefficient in terms of both time and money. This study applied remote sensing techniques to reduce both time and cost. The objective of this study is to use freely accessible Sentinel‑2 multispectral images to extract the depth information. Temporal variation was minimized by comparing the histograms of satellite images obtained over four consecutive months. The sea topography is determined using regression analysis, utilizing samples from reference data. The reference data is adjusted with the changes in shorelines, as the alteration of shorelines serves as a parameter for these modifications. Using the regression coefficients, analyses were conducted in regions with undetermined depths. The bathymetry maps were evaluated against a reference dataset and improved by incorporating shorelines. The analyses were carried out individually over four months, and the derived bathymetric data showed significant monthly average and monthly shoreline changes. The employed methodology offers an alternative approach for bathymetry studies that require temporal resolution when the available reference bathymetric data is insufficient.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"26 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coastal Shoreline Change in Eastern Indian Metropolises","authors":"Vijay K. Kannaujiya, Abhishek K. Rai, Sukanta Malakar","doi":"10.1007/s41064-024-00286-y","DOIUrl":"https://doi.org/10.1007/s41064-024-00286-y","url":null,"abstract":"<p>The coastal regions of India have a high population density and are ecologically productive. However, they are also susceptible to both human activity and natural calamities, which can lead to erosion and accretion. As part of the sustainable management of coastal zones, these threats have taken precedence in evaluating shoreline dynamicity. This study demonstrated the effectiveness of integrating remote sensing and geographic information systems for comprehensive long-term coastal change analyses. The analysis reveals that the mean erosion rate along the Chennai coast ranges from −0.2 to −2.5 m/year. Accretion is also recorded along certain parts of the Chennai coast, with rates ranging from 1 to 4.6 m/year. The Vishakhapatnam shoreline has a consistent pattern of both erosion and accretion, with erosion rates ranging from −0.1 to −6.8 m/year and accretion from 0.2 to 5 m/year. However, most of the Puri coast exhibits an accretion pattern, with values ranging from approximately 0.1 to 3.22 m/year. The fluctuations in shorelines of these three metropolises are a matter of great concern, given that these coastal cities play a substantial part in India’s economic and cultural endeavors. The ongoing occurrence of climate change and global warming has led to an elevation in the worldwide sea level, along with a heightened intensity and frequency of extreme occurrences like tropical cyclones in the Bay of Bengal, where these three coasts are situated. The coastlines of these urban areas may experience alterations due to natural phenomena like rising sea levels and tropical cyclones, as well as a diverse array of human activity. This study may help to facilitate the formulation of suitable management strategies and regulations for the coastal areas of Vishakhapatnam, Puri, Chennai, and other Indian coastal places that have similar physical attributes.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Sea Level Rise Impacts on the Southeastern Türkiye Coastline: a Coastal Vulnerability Perspective","authors":"Fahri Aykut, Devrim Tezcan","doi":"10.1007/s41064-024-00284-0","DOIUrl":"https://doi.org/10.1007/s41064-024-00284-0","url":null,"abstract":"<p>Coastal areas are inherently sensitive and dynamic, susceptible to natural forces like waves, winds, currents, and tides. Human activities further accelerate coastal changes, while climate change and global sea level rise add to the challenges. Recognizing and safeguarding these coasts, vital for both socioeconomic and environmental reasons, becomes imperative. The objective of this study is to categorize the coasts of the Mersin and İskenderun bays along the southeastern coast of Türkiye based on their vulnerability to natural forces and human-induced factors using the coastal vulnerability index (CVI) method. The study area encompasses approximately 520 km of coastline. The coastal vulnerability analysis reveals that the coastal zone comprises various levels of vulnerability along the total coastline: 24.7% (128 km) is categorized as very high vulnerability, 27.4% (142 km) as high vulnerability, 23.7% (123 km) as moderate vulnerability, and 24.3% (126 km) as low vulnerability. Key parameters influencing vulnerability include coastal slope, land use, and population density. High and very high vulnerability are particularly prominent in coastal plains characterized by gentle slopes, weak geological and geomorphological features, and significant socioeconomic value.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"51 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building a Fully-Automatized Active Learning Framework for the Semantic Segmentation of Geospatial 3D Point Clouds","authors":"Michael Kölle, Volker Walter, Uwe Sörgel","doi":"10.1007/s41064-024-00281-3","DOIUrl":"https://doi.org/10.1007/s41064-024-00281-3","url":null,"abstract":"<p>In recent years, significant progress has been made in developing supervised Machine Learning (ML) systems like Convolutional Neural Networks. However, it’s crucial to recognize that the performance of these systems heavily relies on the quality of labeled training data. To address this, we propose a shift in focus towards developing sustainable methods of acquiring such data instead of solely building new classifiers in the ever-evolving ML field. Specifically, in the geospatial domain, the process of generating training data for ML systems has been largely neglected in research. Traditionally, experts have been burdened with the laborious task of labeling, which is not only time-consuming but also inefficient. In our system for the semantic interpretation of Airborne Laser Scanning point clouds, we break with this convention and completely remove labeling obligations from domain experts who have completed special training in geosciences and instead adopt a hybrid intelligence approach. This involves active and iterative collaboration between the ML model and humans through Active Learning, which identifies the most critical samples justifying manual inspection. Only these samples (typically <span>(ll 1{%})</span> of Passive Learning training points) are subject to human annotation. To carry out this annotation, we choose to outsource the task to a large group of non-specialists, referred to as the crowd, which comes with the inherent challenge of guiding those inexperienced annotators (i.e., “short-term employees”) to still produce labels of sufficient quality. However, we acknowledge that attracting enough volunteers for crowdsourcing campaigns can be challenging due to the tedious nature of labeling tasks. To address this, we propose employing paid crowdsourcing and providing monetary incentives to crowdworkers. This approach ensures access to a vast pool of prospective workers through respective platforms, ensuring timely completion of jobs. Effectively, crowdworkers become <i>human processing units</i> in our hybrid intelligence system mirroring the functionality of <i>electronic processing units</i>.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"18 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140580122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}