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2023 Joint Urban remote sensing Event, JURSE 2023

作者： Mei, Qipeng Budde, Lina E. Iwaszczuk, Dorota Technical University of Darmstadt Civil and Environmental Engineering Remote Sensing and Image Analysis Darmstadt Germany

ISBN: (纸本)9781665493734

Renewable energy is a key component for the goal of carbon neutrality. A significant contribution is made by photovoltaic systems. This work combines machine learning and geographical processing to identify photovoltaic systems in aerial images and generate the corresponding vector data with geographic alignment information. For this, a combination of Inception V3 model for classification and a DeepLab V3+ model for segmentation is used to extract photovoltaic systems. In addition, the power generation potential of photovoltaic systems are predicted by a linear regression model. The used model is applied to two datasets from different data sources for the same study area. The segmentation maps archives in both cases a Intersection over Union for the photovoltaic class of at least 75 %. Based on the identified photovoltaic systems the distribution shows a high number in residential areas whereas the highest energy generation potential is in industrial and commercial areas. © 2023 IEEE.

关键词： Antennas

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OCTREE-BASED APPROACH FOR REAL-TIME 3D INDOOR MAPPING USING RGB-D VIDEO DATA 12

OCTREE-BASED APPROACH FOR REAL-TIME 3D INDOOR MAPPING USING ...

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12th International Symposium on Mobile Mapping Technology, MMT 2023

作者： Hou, J. Goebel, M. Hübner, P. Iwaszczuk, D. Technical University of Darmstadt Dept. of Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Darmstadt Germany

3D indoor mapping is becoming increasingly critical for a variety of applications such as path planning and navigation for robots. In recent years, there is a growing interest in how low-cost sensors, such as monocular or depth cameras, can be used for 3D mapping. In our paper, we present an octree-based approach for real-time 3D indoor mapping using a handheld RGB depth camera. One benefit of the generated octree map is that it requires less storage and computational resources than point cloud models. Moreover, it explicitly represents free space and unmapped areas, which are essential for the robot's navigation tasks. In this work, on the basis of the ORBSLAM3 system (Campos et al., 2021), we developed an octree mapping system, which directly calls the keyframes and estimated poses provided by ORB-SLAM3 algorithms. Furthermore, we used point cloud library (PCL) for the dense point cloud mapping and then OctoMap for the point cloud to octree map conversion. Finally, we implemented an efficient probabilistic 3D mapping in the robot operating system (ROS) environment. We used the TUM RGB-D dataset to evaluate the estimated trajectories of the camera. The evaluation shows an average translational RMSE of 5.9 cm on the TUM RGB-D dataset. Besides, we also compared the ground truth point clouds and our generated point clouds. The result shows the mean cloud-to-cloud distance in the corridor scene is about 6 cm. All the evaluation results show our proposed approach is a promising solution for advanced indoor voxel mapping and robotic navigation systems. © Author(s) 2023.

关键词： Cameras

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Polygon-based mapping of photovoltaic systems and estimation of energy generation potential

Polygon-based mapping of photovoltaic systems and estimation...

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Urban remote sensing Joint Event

作者： Qipeng Mei Lina E. Budde Dorota Iwaszczuk Civil and Environmental Engineering Remote Sensing and Image Analysis Technical University of Darmstadt Darmstadt Germany

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Identification of misclassified pixels in semantic segmentation with uncertainty evaluation 24

Identification of misclassified pixels in semantic segmentat...

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2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow

作者： Budde, Lina E. Bulatov, Dimitri Iwaszczuk, Dorota Technical University of Darmstadt Dept. of Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Darmstadt Germany Ettlingen Germany

Classification, and in particular semantic segmentation, plays a major role in remote sensing. In remote sensing, the classes usually correspond to landcover or landuse types while the data elements are image pixels. The results are so-called semantically segmented pixels describing the content of the data for each pixel. The identification of misclassified pixels is essential to perceive the overall performance of the classification algorithm. In the case of semantic segmentation, it is typically done with ground truth labels. However, such ground truth labels are rare and mostly reserved for training only. Especially deep learning approaches are data-hungry algorithms requesting a lot of labeled examples. In this work, we explore the possibility of using Monte-Carlo dropout for the identification of model-induced misclassifications. In particular, we obtain uncertainty measures from several inferences induced by the Monte-Carlo dropout. Furthermore, we examine how Markov Random Field optimization can reduce the number of misclassifications and facilitate their identification. The extent to which uncertainties provide information about misclassifications is assessed. Our results allow detecting 51% of the misclassifications using uncertainties. Application of Markov Random Field optimization leads to a reduction of the percentage of misclassifications while detecting 0.4% more misclassifications as without. © 2021 International Archives of the Photogrammetry, remote sensing and Spatial Information Sciences - ISPRS Archives. All rights reserved.

关键词： Pixels

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Investigation on Relative Pose Regression for Multi-Camera Setups with Neural Radiance Fields 8

Investigation on Relative Pose Regression for Multi-Camera S...

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8th International ISPRS Workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Kullmann, Timo Hübner, Patrick Wirth, Tristan Kuijper, Arjan Iwaszczuk, Dorota Technical University of Darmstadt Dept. of Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Mathematical and Applied Visual Computing Darmstadt Germany Fraunhofer IGD Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Interactive Graphics Systems Darmstadt Germany

Neural Radiance Fields (NeRFs) are a novel approach that is being intensively investigated in 3D scene reconstruction and similar fields to overcome challenges of conventional methods. In this paper, we address the problem of estimating missing camera poses in a six degrees of freedom setting, pushing the capabilities of NeRFs to address scenarios where only the primary camera's pose is known. Specifically, we focus on dual-camera setups with this constraint. Our core contribution is a novel pose correction model that operates alongside an unmodified NeRF model, for which we have chosen Nerfacto in the Nerfstudio framework. The pose correction model learns the necessary relative translation and rotation adjustments for the secondary camera solely through the NeRF loss. This allows us to integrate our correction model directly into the Nerfacto training pipeline without altering the core functionality. Through extensive experiments on different camera configurations in a synthetic scene, we rigorously evaluate our model's performance across diverse scenarios, pushing it to its limits. Our findings reveal that our model can effectively learn pose correction parameters within a constrained range, with increased sensitivity to larger translations and particular challenges in rotation corrections. This research highlights the potential of NeRFs for machine learning-driven 3D reconstruction on dual- and multi-camera platforms, expanding the applicability of NeRFs to more complex, real-world setups despite the inherent challenges. Copyright: © 2024 Timo Kullmann et al.

关键词： 3D reconstruction

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Influence of Out-of-Distribution Examples on the Quality of Semantic Segmentation in remote sensing

Influence of Out-of-Distribution Examples on the Quality of ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Kevin Qiu Dimitri Bulatov Lina E. Budde Timo Kullmann Dorota Iwaszczuk Scene Analysis Division Fraunhofer Institute of Optronics System Technologies and Image Exploitation Ettlingen Germany Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Technical University of Darmstadt Darmstadt Germany

Semantic segmentation for land cover maps follows the closed world assumption, where each pixel must be classified into a set of predefined classes. In order to fulfill this assumption, an additional class is usually introduced to describe all areas not covered by the main classes, called "clutter" or "other". Consequently, this class is extremely heterogeneous, and the classification is usually subpar. Using a common approach for uncertainty assessment of land cover classification, we analyze the influence of the clutter class being present or absent during training on the semantic segmentation. We assess the model uncertainties of two different deep learning models, U-Net and DeepLab V3+, and different training configurations by using a Monte-Carlo dropout based uncertainty metric. The corresponding uncertainty maps and histograms show a correlation between clutter class and the uncertainty metric.

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Polygonizing roof segments from high-resolution aerial images using YOLOv8-based edge detection

arXiv

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arXiv 2025年

作者： Mei, Qipeng Bulatov, Dimitri Iwaszczuk, Dorota Remote Sensing and Image Analysis Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Franziska-Braun-Str. 7 Darmstadt64287 Germany Fraunhofer IOSB Ettlingen Gutleuthausstrasse 1 Ettlingen76275 Germany

This study presents a novel approach for roof detail extraction and vectorization using remote sensing images. Unlike previous geometric-primitive-based methods that rely on the detection of corners, our method focuses on edge detection as the primary mechanism for roof reconstruction, while utilizing geometric relationships to define corners and faces. We adapt the YOLOv8 OBB model, originally designed for rotated object detection, to extract roof edges effectively. Our method demonstrates robustness against noise and occlusion, leading to precise vectorized representations of building roofs. Experiments conducted on the SGA and Melville datasets highlight the method’s effectiveness. At the raster level, our model outperforms the state-of-the-art foundation segmentation model (SAM), achieving a mIoU between 0.85 and 1 for most samples and an ovIoU close to 0.97. At the vector level, evaluation using the Hausdorff distance, PolyS metric, and our raster-vector-metric demonstrates significant improvements after polygonization, with a close approximation to the reference data. The method successfully handles diverse roof structures and refines edge gaps, even on complex roof structures of new, excluded from training datasets. Our findings underscore the potential of this approach to address challenges in automatic roof structure vectorization, supporting various applications such as urban terrain *** Codes 68T10, 68U10 © 2025, CC BY-NC-ND.

关键词： Aerial photography

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DEEPLIO: DEEP LIDAR INERTIAL SENSOR FUSION for ODOMETRY ESTIMATION 24

DEEPLIO: DEEP LIDAR INERTIAL SENSOR FUSION for ODOMETRY ESTI...

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2021 24th ISPRS Congress "Imaging Today, Foreseeing Tomorrow", Commission I

作者： Javanmard-Gh, Arash Iwaszczuk, Dorota Roth, Stefan Remote Sensing and Image Analysis Dept. of Civil and Environmental Engineering Sciences Technical University of Darmstadt Germany Visual Inference Lab Dept. of Computer Science Technical University of Darmstadt Germany

Having a good estimate of the position and orientation of a mobile agent is essential for many application domains such as robotics, autonomous driving, and virtual and augmented reality. In particular, when using LiDAR and IMU sensors as the inputs, most existing methods still use classical filter-based fusion methods to achieve this task. In this work, we propose DeepLIO, a modular, end-to-end learning-based fusion framework for odometry estimation using LiDAR and IMU sensors. For this task, our network learns an appropriate fusion function by considering different modalities of its input latent feature vectors. We also formulate a loss function, where we combine both global and local pose information over an input sequence to improve the accuracy of the network predictions. Furthermore, we design three sub-networks with different modules and architectures derived from DeepLIO to analyze the effect of each sensory input on the task of odometry estimation. Experiments on the benchmark dataset demonstrate that DeepLIO outperforms existing learning-based and model-based methods regarding orientation estimation and shows a marginal position accuracy difference. © Author(s) 2021.

关键词： Mobile agents

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A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform

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International Journal of Digital Earth 2023年第1期16卷 988-1007页

作者： Jie Zhou Massimo Menenti Li Jia Bo Gao Feng Zhao Yilin Cui Xuqian Xiong Xuan Liu Dengchao Li Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province College of Urban and Environmental SciencesCentral China Normal UniversityWuhanPeople’s Republic of China State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China Faculty of Civil Engineering and Geosciences Delft University of TechnologyDelftThe Netherlands College of Resources Environment and Tourism Capital Normal UniversityBeijingPeople’s Republic of China The First Geological brigade of Hubei Geological Bureau HuangshiPeople’s Republic of China

Spatiotemporal residual noise in terrestrial earth observation products,often caused by unfavorable atmospheric conditions,impedes their broad *** users prefer to use gap-filled remote sensing products with time series reconstruction(TSR)*** currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage *** study introduces a new open-source software package entitled‘HANTS-GEE’that implements a well-known and robust TSR algorithm,*** analysis of Time Series(HANTS),on the Google Earth Engine(GEE)platform for scalable reconstruction of terrestrial earth observation *** tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on *** to site-based and regional-based case evaluation,the new tool can effectively eliminate cloud contamination in the time series of earth observation *** with traditional PC-based HANTS implementation,the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated *** HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume,promoting algorithm usage by much broader user *** our knowledge,the software package is thefirst tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.

关键词： Time series reconstruction remote sensing Google Earth Engine HANTS gap-filling

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UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for remotely Sensed imagery Interpretation

arXiv

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arXiv 2024年

作者： Zhang, Pengfei Li, Chang Zhang, Yongjun Qin, Rongjun Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Science Central China Normal University Wuhan China School of Remote Sensing and Information Engineering Wuhan University China Department of Civil Environmental and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio State University ColumbusOH United States

remotely sensed imagery interpretation (RSII), including semantic segmentation and change detection, faces the three major problems: (1) objective representation of spatial distribution patterns with coexistence of spatial stationary and non-stationary;(2) edge uncertainty problem caused by downsampling encoder and intrinsic edge noises (e.g., mixed pixel and edge occlusion etc.);and (3) false detection problem caused by geometric registration error in change detection. To solve the aforementioned problems, uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed, whose superiorities are as follows: (1) a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP) is proposed to enhance the interaction of spatially frequency-wise stationary and non-stationary features to yield high-fidelity edge extraction result. Inspired by HRFormer, SHCP proposes high-frequency-wise stream to replace high-resolution-wise stream in HRFormer through the whole encoder-decoder process with parallel frequency-wise high-to-low streams, so it improves the edge extraction accuracy by continuously remaining high-frequency information;(2) a mask-and-geo-knowledge-based uncertainty diffusion module (MUDM), which is a self-supervised learning strategy, is proposed to improve the edge accuracy of extraction and change detection by gradually removing the simulated spectrum noises based on geo-knowledge and the generated diffused spectrum noises;(3) a frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection. Besides the aforementioned spectrum noises in semantic segmentation, MUDM is also a self-supervised learning strategy to effectively reduce the edge false change detection from the generated imagery with geometric registration error. In semantic segmentation experiments, UDHF2-Net achieved the best mIoU values of 92.00%, 87.31% and 55.27% respecti

关键词： Semantic Segmentation

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