Analysis of regional large-gradient land subsidence in the Alto Guadalentín Basin (Spain) using open-access aerial LiDAR datasets

Land subsidence associated with groundwater overexploitation in the Alto Guadalentín Basin (Spain) aquifer system has been detected during the last decades. In this work, for the first time, we propose a new point cloud differencing methodology to detect land subsidence at basin scale, based on the multiscale model-to-model cloud comparison (M3C2) algorithm. This method is applied to two open-access airborne LiDAR datasets acquired in 2009 and 2016, respectively. First the internal edge connection errors in the different flight lines were addressed by means of a smoothing point cloud method. LiDAR datasets capture information from ground and non-ground points. Therefore, a method combining gradient filtering and cloth simulation filtering (CSF) algorithms was applied to remove non-ground points. The iterative closest point (ICP) algorithm was used for point cloud registration of both point clouds exhibiting a very stable and robust performance. The results show that vertical deformation rates are up to −14 cm/year in the basin from 2009 to 2016, in agreement with the displacement reported by previous studies. LiDAR results have been compared to the velocity measured by continuous GNSS stations and an InSAR dataset. For the GNSS-LiDAR and InSAR-LiDAR comparison, we computed a common 100 × 100 m grid in order to assess any similarities and discrepancies. The results show a good agreement between the vertical displacements obtained from the three different surveying techniques. Furthermore, LiDAR results were compared with the distribution of compressible soil thickness showing a clear relationship. The study underlines the potential of open-access and non-customized LiDAR to monitor the distribution and magnitude of vertical deformations in areas prone to be affected by groundwater-withdrawal-induced land subsidence.This research was funded by the ESA-MOST China DRAGON-5 project (ref. 59339) and by a Chinese Scholarship Council studentship awarded to Liuru Hu (Ref. 202004180062). María I. Navarro-Hernández and Guadalupe Bru are funded by the PRIMA programme supported by the European Union under grant agreement No 1924, project RESERVOIR

Continuous Modeling of 3D Building Rooftops From Airborne LIDAR and Imagery

In recent years, a number of mega-cities have provided 3D photorealistic virtual models to support the decisions making process for maintaining the cities' infrastructure and environment more effectively. 3D virtual city models are static snap-shots of the environment and represent the status quo at the time of their data acquisition. However, cities are dynamic system that continuously change over time. Accordingly, their virtual representation need to be regularly updated in a timely manner to allow for accurate analysis and simulated results that decisions are based upon. The concept of "continuous city modeling" is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. However, developing a universal intelligent machine enabling continuous modeling still remains a challenging task. Therefore, this thesis proposes a novel research framework for continuously reconstructing 3D building rooftops using multi-sensor data. For achieving this goal, we first proposes a 3D building rooftop modeling method using airborne LiDAR data. The main focus is on the implementation of an implicit regularization method which impose a data-driven building regularity to noisy boundaries of roof planes for reconstructing 3D building rooftop models. The implicit regularization process is implemented in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). Secondly, we propose a context-based geometric hashing method to align newly acquired image data with existing building models. The novelty is the use of context features to achieve robust and accurate matching results. Thirdly, the existing building models are refined by newly proposed sequential fusion method. The main advantage of the proposed method is its ability to progressively refine modeling errors frequently observed in LiDAR-driven building models. The refinement process is conducted in the framework of MDL combined with HAT. Markov Chain Monte Carlo (MDMC) coupled with Simulated Annealing (SA) is employed to perform a global optimization. The results demonstrates that the proposed continuous rooftop modeling methods show a promising aspects to support various critical decisions by not only reconstructing 3D rooftop models accurately, but also by updating the models using multi-sensor data

Integration of LiDAR and photogrammetric data for enhanced aerial triangulation and camera calibration

PhD ThesisThe integration of complementary airborne light detection and ranging (LiDAR) and photogrammetric data continues to receive attention from the relevant research communities. Such an approach requires the optimized registration of the two data types within a common coordinate reference frame and thus enables the cross-calibration of one information source against another. This research assumes airborne LiDAR as a reference dataset against which in-flight camera system calibration and validation can be performed. The novel methodology involves the production of dense photogrammetric point clouds derived using the simultaneous adjustment of GNSS/IMU data and a dense set of photogrammetric tie points. Quality of the generated photogrammetric dataset is further improved through introducing the self-calibration additional parameters in the combined adjustment. A robust least squares surface matching algorithm is then used to minimise the Euclidean distances between the two datasets. After successful matching, well distributed LiDAR-derived control points (LCPs) are automatically identified and extracted. Adjustment of the photogrammetric data is then repeated using extracted LCPs in a self-calibrating bundle adjustment. The research methodology was tested using two datasets acquired using different photogrammetric digital sensor systems, a Microsoft UltraCamX large format camera and an Applanix DSS322 medium format camera. Systematic sensitivity testing included the influence of the number and weighting of LCPs required to achieve optimised adjustment. For the UltraCamX block it was found that when the number of control points exceeded 80, the accuracy of the adjustment stabilized at c. 2 cm in all axes, regardless of point weighting. Results were also compared with those from reference calibration using surveyed ground control points in the test area, with good agreement found between the two. Similar results were obtained for the DSS322 block, with block accuracy stabilizing at 100 LCPs. Moreover, for the DSS322 camera, introducing self-calibration greatly improved the accuracy of aerial triangulation

Evaluation of Long-Range Mobile Mapping System (MMS) and Close-Range Photogrammetry for Deformation Monitoring. A Case Study of Cortes de Pallás in Valencia (Spain)

none8openDi Stefano, Francesco; Cabrelles, Miriam; García-Asenjo, Luis; Lerma, José Luis; Malinverni, Eva Savina; Baselga, Sergio; Garrigues, Pascual; Pierdicca, RobertoDi Stefano, Francesco; Cabrelles, Miriam; García-Asenjo, Luis; Lerma, José Luis; Malinverni, Eva Savina; Baselga, Sergio; Garrigues, Pascual; Pierdicca, Robert

Automated Building Information Extraction and Evaluation from High-resolution Remotely Sensed Data

The two-dimensional (2D) footprints and three-dimensional (3D) structures of buildings are of great importance to city planning, natural disaster management, and virtual environmental simulation. As traditional manual methodologies for collecting 2D and 3D building information are often both time consuming and costly, automated methods are required for efficient large area mapping. It is challenging to extract building information from remotely sensed data, considering the complex nature of urban environments and their associated intricate building structures. Most 2D evaluation methods are focused on classification accuracy, while other dimensions of extraction accuracy are ignored. To assess 2D building extraction methods, a multi-criteria evaluation system has been designed. The proposed system consists of matched rate, shape similarity, and positional accuracy. Experimentation with four methods demonstrates that the proposed multi-criteria system is more comprehensive and effective, in comparison with traditional accuracy assessment metrics. Building height is critical for building 3D structure extraction. As data sources for height estimation, digital surface models (DSMs) that are derived from stereo images using existing software typically provide low accuracy results in terms of rooftop elevations. Therefore, a new image matching method is proposed by adding building footprint maps as constraints. Validation demonstrates that the proposed matching method can estimate building rooftop elevation with one third of the error encountered when using current commercial software. With an ideal input DSM, building height can be estimated by the elevation contrast inside and outside a building footprint. However, occlusions and shadows cause indistinct building edges in the DSMs generated from stereo images. Therefore, a “building-ground elevation difference model” (EDM) has been designed, which describes the trend of the elevation difference between a building and its neighbours, in order to find elevation values at bare ground. Experiments using this novel approach report that estimated building height with 1.5m residual, which out-performs conventional filtering methods. Finally, 3D buildings are digitally reconstructed and evaluated. Current 3D evaluation methods did not present the difference between 2D and 3D evaluation methods well; traditionally, wall accuracy is ignored. To address these problems, this thesis designs an evaluation system with three components: volume, surface, and point. As such, the resultant multi-criteria system provides an improved evaluation method for building reconstruction

Sensing Mountains

Sensing mountains by close-range and remote techniques is a challenging task. The 4th edition of the international Innsbruck Summer School of Alpine Research 2022 – Close-range Sensing Techniques in Alpine Terrain brings together early career and experienced scientists from technical-, geo- and environmental-related research fields. The interdisciplinary setting of the summer school creates a creative space for exchanging and learning new concepts and solutions for mapping, monitoring and quantifying mountain environments under ongoing conditions of change

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application