ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Journal: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Abbreviation

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Publisher

Copernicus

ISSN

2194-9042
2194-9050

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Publications 1 - 10 of 33

Assessing the alignment between geometry and colors in TLS colored point
Wang, Zhaoyi; Varga, Matej; Medic, Tomislav; et al. (2023)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The integration of the color information from RGB cameras with the point cloud geometry is used in numerous applications. However, little attention has been paid on errors that occur when aligning colors to points in terrestrial laser scanning (TLS) point clouds. Such errors may impact the performance of algorithms that utilize colored point clouds. Herein, we propose a procedure for assessing the alignment between the TLS point cloud geometry and colors. The procedure is based upon identifying artificial targets observed in both LiDAR-based point cloud intensity data and camera-based RGB data, and quantifying the quality of the alignment using differences between the target center coordinates estimated separately from these two data sources. Experimental results with eight scanners show that the quality of the alignment depends on the scanner, the software used for colorizing the point clouds, and may change with changing environmental conditions. While we found the effects of misalignment to be negligible for some scanners, they exhibited clearly systematic patterns exceeding the beam divergence, image and scan resolution for four of the scanners. The maximum deviations were about 2 mrad perpendicular to the line-of-sight when colorizing the point clouds with the respective manufacturer’s software or scanner in-built functions, while they were up to about 5 mrad when using a different software. Testing the alignment quality, e.g., using the approach presented herein, is thus important for applications requiring accurate alignment of the RGB colors with the point cloud geometry.
A Cnn-based flood mapping approach using sentinel-1 data
Tavus, Beste; Can, Recep; Kocaman, Sultan (2022)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The adverse effects of flood events have been increasing in the world due to the increasing occurrence frequency and their severity due to urbanization and the population growth. All weather sensors, such as satellite synthetic aperture radars (SAR) enable the extent detection and magnitude analysis of such events under cloudy atmospheric conditions. Sentinel-1 satellite from European Space Agency (ESA) facilitate such studies thanks to the free distribution, the regular data acquisition scheme and the availability of open source software. However, various difficulties in the visual interpretation and processing exist due to the size and the nature of the SAR data. The supervised machine learning algorithms have increasingly been used for automatic flood extent mapping. However, the use of Convolutional Neural Networks (CNNs) for this purpose is relatively new and requires further investigations. In this study, the U-Net architecture for multi-class segmentation of flooded areas and flooded vegetation was employed by using Sentinel-1 SAR data and altitude information as input. The training data was produced by an automatic thresholding approach using OTSU method in Sardoba, Uzbekistan and Sagaing, Myanmar. The results were validated in Ordu, Turkey and in Ca River, Vietnam by visual comparison with previously produced flood maps. The results show that CNNs have great potential in classifying flooded areas and flooded vegetation even when trained in areas with different geographical setting. The F1 scores obtained in the study for flood and flooded vegetation classes were 0.91 and 0.85, respectively.
Simultaneous Detection and Tracking of Pedestrian from Velodyne Laser Scanning Data
Xiao, Wen; Vallet, Bruno; Schindler, Konrad; et al. (2016)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, Nearest-point and Max-distance. Then, all the points on moving objects are transferred into a space-time (x, y, t) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.
On the effect of dem quality for landslide susceptibility mapping
Karakas, Gizem; Kocaman, Sultan; Gokceoglu, Candan (2022)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Generating precise and up-To-date landslide susceptibility maps (LSMs) in landslide-prone areas is important to identify hazard potential in the future. The data quality and the method selection affect the accuracy of the LSMs. In this context, the accuracy and precision of the digital elevation models (DEMs) used as input are among the most important performance elements. Therefore, the influence of DEM accuracy and spatial resolution in producing LSMs was investigated here. A high accuracy DEM with 5 m grid spacing produced from aerial photographs and the EU-DEM v1.1 freely accessible from Copernicus Land Monitoring Service with 25 m spatial resolution were used for producing two different LSMs using the Random Forest (RF) method in this study. The RF method has proven success for this purpose. A total of eight conditioning factors, which include topographical and geological features, was used as model input. The landslide inventory was derived with the help of aerial stereo images with 20 cm and 30 cm ground sampling distances. The performances of the LSMs were assessed with receiver operating characteristics (ROC) area under curve (AUC) values. In addition, the results were compared with visual inspection. The results show that although the AUC values obtained from the aerial DEM (0.95) and EU-DEM v1.1 (0.93) were comparable; based on the visual assessments, the LSM obtained from the higher resolution DEM was found more successful in detecting the landslides and thus exhibited better prediction performance.
Supervised outlier detection in large-scale MVS point clouds for 3D city modeling applications
Stucker, Corinne; Richard, Audrey; Wegner, Jan D.; et al. (2018)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images. What makes outlier removal hard are varying distributions of inliers and outliers across a scene. Heuristic outlier removal using a specific feature that encodes point distribution often delivers unsatisfying results. Although most outliers can be identified correctly (high recall), many inliers are erroneously removed (low precision), too. This aggravates object 3D reconstruction due to missing data. We thus propose to discriminatively learn class-specific distributions directly from the data to achieve high precision. We apply a standard Random Forest classifier that infers a binary label (inlier or outlier) for each 3D point in the raw, unfiltered point cloud and test two approaches for training. In the first, non-semantic approach, features are extracted without considering the semantic interpretation of the 3D points. The trained model approximates the average distribution of inliers and outliers across all semantic classes. Second, semantic interpretation is incorporated into the learning process, i.e. we train separate inlieroutlier classifiers per semantic class (building facades, roof, ground, vegetation, fields, and water). Performance of learned filtering is evaluated on several large SfM point clouds of cities. We find that results confirm our underlying assumption that discriminatively learning inlier-outlier distributions does improve precision over global heuristics by up to ≈ 12 percent points. Moreover, semantically informed filtering that models class-specific distributions further improves precision by up to ≈ 10 percent points, being able to remove very isolated building, roof, and water points while preserving inliers on building facades and vegetation.
AGBD: A Global-scale Biomass Dataset
Sialelli, Ghjulia; Peters, Torben; Wegner, Jan Dirk; et al. (2025)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanitys biggest challenges: climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they focus on specific, local regions at high resolution, or they offer global coverage at low resolution. There is a need for a machine learning-ready, globally representative, high-resolution benchmark dataset. Our findings indicate significant variability in biomass estimates across different vegetation types, emphasizing the necessity for a dataset that accurately captures global diversity. To address these gaps, we introduce a comprehensive new dataset that is globally distributed, covers a range of vegetation types, and spans several years. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. We also produce a dense, high-resolution (10 m) map of AGB predictions for the entire area covered by the dataset. Rigorously tested, our dataset is accompanied by several benchmark models and is publicly available. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation.
Towards Assessing Sandstone Surface Moisture and Degradation Level from Radiometrically Corrected TLS Intensity Data
Laasch, Helena; Medic, Tomislav; Wieser, Andreas (2023)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Water is a prevalent deterioration agent for historic masonry works, especially those made of clay-bearing sandstones. To preserve cultural heritage made of sandstone, it is important to monitor, and then detect the regions with water retention or stone deterioration. To that aim, we investigate the prospects of terrestrial laser scanner (TLS) intensities for quantifying moisture in sandstone. Through a series of experiments following the drying processes of sandstone samples, we verify that TLS intensities can serve as moisture proxies for remote-sensing water retention. We identify the theoretically most suitable wavelengths, systematic effects requiring mitigation, and promising mitigation strategies. However, we also observe that the intensities are significantly affected by the type of sandstone and its level of degradation. Our results indicate that it is possible to distinguish different sandstones and levels of artificial degradation by observing and analyzing TLS-intensity time series during the drying process.
Gaze and feet as additional input modalities for interacting with geospatial interfaces
Çöltekin, Arzu; Hempel, Julia; Brychtova, Alzbeta; et al. (2016)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Geographic Information Systems (GIS) are complex software environments and we often work with multiple tasks and multiple displays when we work with GIS. However, user input is still limited to mouse and keyboard in most workplace settings. In this project, we demonstrate how the use of gaze and feet as additional input modalities can overcome time-consuming and annoying mode switches between frequently performed tasks. In an iterative design process, we developed gaze- and foot-based methods for zooming and panning of map visualizations. We first collected appropriate gestures in a preliminary user study with a small group of experts, and designed two interaction concepts based on their input. After the implementation, we evaluated the two concepts comparatively in another user study to identify strengths and shortcomings in both. We found that continuous foot input combined with implicit gaze input is promising for supportive tasks.
Self-Supervised Adversarial Shape Completion
Peters, Torben; Schindler, Konrad; Brenner, Claus (2022)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in a self-supervised fashion, only from incomplete examples. This is enabled by a discriminator network that rejects incomplete shapes, via a loss function that separately assesses local sub-regions of the generated example and accepts only regions with sufficiently high point count. This inductive bias against empty regions forces the generator to output complete shapes. We demonstrate the effectiveness of this approach on synthetic data from ShapeNet and ModelNet, and on a real mobile mapping dataset with nearly 9'000 incomplete cars. Moreover, we apply it to the KITTI autonomous driving dataset without retraining, to highlight its ability to generalise to different data characteristics.
XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission II
(2022)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Publications 1 - 10 of 33

Journal: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

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