Extracting Terrain Points from Airborne Laser Scanning Data in Densely Forested Areas

Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m

Comparative software analysis to obtain DTM with RPAS photogrammetry

[EN] RPAS (Remotely Piloted Aircraft Systems) are widely used in photogrammetry for taking images due to their high spatial resolution and speed of response, being able to reach areas of difficult access, being important to design a good procedure in the field to minimize errors in data collection. It is recommended to use ground control points (GCP) using conventional RPAS, when they do not work with RTK (Real Time Kinematic) systems. Likewise, there are free and licensed photogrammetric programs on the market to generate digital surface models (DSM), terrain models (DTM) and orthophotomosaics. In this article, two photogrammetric programs are used to process images captured with RPAS, such as Agisoft Metashape and Recap Photo, using GCP and check points. The study was carried out in Almenara (Spain) where the topographic survey was carried out with RPAS, capturing 100 digital images, in an area of 0.38 km2. 6 GCP were used in order to orient the digital images well in the local coordinate system and to properly georeference the images obtained during the flight. To obtain the MDT, the CloudCompare software was used to filter the cloud of points obtained from both software. The results show a difference in height between the two DTMs of less than 28 cm, taking as a reference the DTM of the Agisoft Metashape point cloud and regarding the error in the check points, Recap Photo presented a greater error.[ES] Los RPAS (Sistemas de aeronaves pilotados a distancia) son muy utilizados en fotogrametría para la toma de imágenes por su alta resolución espacial y rapidez de respuesta, pudiendo llegar a zonas de difícil acceso, siendo importante diseñar un buen procedimiento en campo para minimizar los errores en la toma de datos. Se recomienda utilizar puntos de apoyo (PA) terrestres utilizando RPAS convencional, que no trabajan con sistemas RTK (Real Time Kinematic). Asimismo, existen en el mercado programas fotogramétricos libres y licenciados para generar modelos digitales de superficie (MDS), del terreno (MDT) y ortofotomosáicos. En este artículo se utilizan dos programas fotogramétricos para procesar imágenes capturadas con RPAS como son Agisoft Metashape y Recap Photo, utilizando puntos de apoyo y control terrestre. El estudio se llevó a cabo en Almenara (España) donde se hizo el levantamiento topográfico con RPAS, capturándose 100 imágenes digitales, en un área de 0.38 km2. Se utilizaron 6 PA con la finalidad de orientar bien las imágenes digitales en el sistema de coordenadas local y realizar de forma adecuada la georreferenciación de las imágenes obtenidas durante el vuelo. Para la obtención del MDT se utilizó el software CloudCompare para hacer el filtrado en la nube de puntos obtenidas de ambos softwares. Los resultados muestran una diferencia en altura entre los dos MDT menor a 28 cm tomando como referencia el MDT de la nube de puntos de Agisoft metashape y en cuanto al error en los puntos apoyo Recap Photo presento mayor error.Esta investigación se ha sufragado parcialmente por el proyecto de la AEI DEEP-MAPS (RTI2018-93874-BI00).Arevalo Verjel, AN.; Lerma García, JL.; Fernández, J. (2021). Análisis comparativo de software para obtener MDT con fotogrametría RPAS. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 209-215. https://doi.org/10.4995/CiGeo2021.2021.12764OCS20921

A Point Cloud Filtering Approach to Generating DTMs for Steep Mountainous Areas and Adjacent Residential Areas

Digital terrain models (DTMs) are considered important basic geographic data. They are widely used in the fields of cartography, land utilization, urban planning, communications, and remote sensing. Digital photogrammetry mainly based on stereo image matching is a frequently applied technique to generate DTMs. Generally, the process of ground filtering should be applied to the point cloud derived from image matching to separate terrain and off-terrain points before DTM generation. However, many of the existing filtering methods perform unsatisfactorily for steep mountainous areas, particularly when residential neighborhoods exist in the proximity of the test areas. In this study, an improved automated filtering method based on progressive TIN (triangulated irregular networks) densification (PTD) is proposed to generate DTMs for steep mountainous areas and adjacent residential areas. Our main improvement on the classic method is the acquisition of seed points with better distribution and reliability to enhance its adaptability to different types of terrain. A rule-based method for detecting ridge points is first applied. The detected points are used as additional seed points. Subsequently, a locally optimized seed point selection method based on confidence interval estimation theory is applied to remove the erroneous points. The experiments on two sets of stereo-matched point clouds indicate that the proposed method performs well for both residential and mountainous areas. The total accuracy values in the form of root-mean-square errors of the generated DTMs by the proposed method are 0.963 and 1.007 m; respectively; which are better than the 1.286 and 1.309 m achieved by the classic PTD method

La Détection des changements tridimensionnels à l'aide de nuages de points : Une revue

peer reviewedChange detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.11. Sustainable cities and communitie

Filtering of LiDAR point cloud with respect to creating precise DEM : a performance analysis of two selected point cloud SW-packages

Reguleringsplanlegging av modellbaserte veiprosjekter krever digitale terrengmodeller med god høydenøyaktighet. Slike modeller opprettes fra bakkepunkter i punktskyer fra laserskanning, som beskriver både terrengoverflate og objekter i et aktuelt område. Det er derfor nødvendig med gode filtreringsalgoritmer for klassifisering av terrengoverflaten. Formålet til dette forsøket er å undersøke egnetheten punktskybehandlingssystemene ALDPAT og QTM har til filtrering av LiDAR-punktskyer. Denne filtreringen er gjort med hensyn til opprettelse av digitale terrengmodeller til reguleringsplanleggingsfasen for modellbaserte veiprosjekter etter håndbok V770 Modellgrunnlag fra Vegdirektoratet. Grenstøl i Tvedestrand kommune, innenfor avgrensningen til veiprosjektet E18 Tvedestrand – Arendal, som stod ferdig i 2019 er valgt som forsøksområde. Fem testområder som representerer terreng og vegetasjonsvariasjonene i det aktuelle området er valgt. Tre av dem er videre klassifisert som vegetasjon og to som harde flater. Datasettene som benyttes stammer fra laserskanning fra helikopter utført i forbindelse med oppstart for utbyggingen av prosjektet i 2016. Disse består av en punktsky per testområde, og en DTM for hele området, som er hentet fra forvaltningsløsningen høydedata.no. DTM er brukt som sammenligningsgrunnlag, mens punktskyene er filtrert med fire ulike filtreringsalgoritmer i ALDPAT, og med en filtreringsalgoritme i QTM. Videre er det foretatt geometrisk kontroll av høydeavvikene mellom DTM-er opprettet fra de filtrerte punktskyene og sammenligningsgrunnlaget. Resultatene fra den geometriske kontrollen viser god evne til filtrering for begge punktskybehandlingssystemene. For alle filtrene er det i enkelte testområder høy prosentandel grove feil. Ett av de to harde testområdene ble som følge av dette utelukket. For det gjenværende harde testområdet presterer QTM aller best. QTM virker også å prestere svært godt i de vegeterte områdene, men ulike filtreringsalgoritmer i ALDPAT viser antydninger til å egne seg bedre. Med bakgrunn i benyttede datasett, testområder og resultater fra geometrisk kontroll av høydeavvik er det indikasjoner til at QTM er det totalt sett best egnede punktskybehandlingssystemet til formålet.Zoning planning for model-based road projects are dependent on good digital elevation models with high absolute height accuracy. Digital elevation models are created from ground points in point clouds derived from laser scanning. These contain many points that describe both the terrain surface and objects. Good filtering algorithms for classification of the terrain surface in point clouds are therefore necessary. The target of this survey is to examine the filtering suitability for the point cloud software packages ALDPAT and QTM. The filtering is done with respect to creating digital elevation models used for zoning planning for model-based road projects according to handbook V770 Modellgrunnlag from The Norwegian Public Roads Administration.M-GEO

Extraction of Digital Terrain Models from Airborne Laser Scanning Data based on Transfer-Learning

With the rapid urbanization, timely and comprehensive urban thematic and topographic information is highly needed. Digital Terrain Models (DTMs), as one of unique urban topographic information, directly affect subsequent urban applications such as smart cities, urban microclimate studies, emergency and disaster management. Therefore, both the accuracy and resolution of DTMs define the quality of consequent tasks. Current workflows for DTM extraction vary in accuracy and resolution due to the complexity of terrain and off-terrain objects. Traditional filters, which rely on certain assumptions of surface morphology, insufficiently generalize complex terrain. Recent development in semantic labeling of point clouds has shed light on this problem. Under the semantic labeling context, DTM extraction can be viewed as a binary classification task. This study aims at developing a workflow for automated point-wise DTM extraction from Airborne Laser Scanning (ALS) point clouds using a transfer-learning approach on ResNet. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and accuracy assessment. First, each point is transformed into a feature image based on its elevation differences with neighbouring points. Then, the feature images are classified into ground and non-ground using ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Lastly, the proposed workflow is compared with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progress TIN Densification (PTD). Results show that the proposed workflow establishes an advantageous accuracy of DTM extraction, which yields only 0.522% Type I error, 4.84% Type II error and 2.43% total error. In comparison, Type I, Type II and total error for PMF are 7.82%, 11.6%, and 9.48%, for PTD are 1.55%, 5.37%, and 3.22%, respectively. The root mean squared error of interpolated DTM of 1 m resolution is only 7.3 cm. Moreover, the use of pre-trained weights largely accelerated the training process and enabled the network to reach unprecedented accuracy even on a small amount of training set. Qualitative analysis is further conducted to investigate the reliability and limitations of the proposed workflow

Detection of anthropogenic terrain features in the summit area of the Luční and Studniční hora mountains (Krkonoše) with use of airborne laser scanning data

Diplomová práce se zabývá detekcí objektů mikroreliéfu s využitím dat leteckého laserového skenování. Cílem práce bylo navrhnout vlastní metodu pro detekci terénních anomálií a dále identifikovat a popsat terénní útvary, které odpovídají antropogenním zásahům v oblasti arkto- alpínské tundry ve vrcholové oblasti Luční a Studniční hory v Krkonoších. Na základě existujících studií byla navržena nová metoda detekce specifických objektů mikroreliéfu, která vychází z kombinace dvou typů dat, tj. bodového mračna a z něj vycházející rastrové reprezentace. Nejprve byly z digitálního modelu reliéfu odvozeny polygonové aproximace terénních anomálií, jejichž prostorové vymezení bylo následně lokálně zpřesněno s využitím bodových mračen. Výstupem je nejúplnější a nejpřesnější polygonové vymezení terénních útvarů, které bylo, s ohledem na dostupná data a specifičnost území a v porovnání s doposud realizovanými studiemi, zatím provedeno. Navrženým algoritmem bylo oproti referenčním datům identifikováno o stovky terénních objektů více. V tomto počtu jsou ale zahrnuty i falešně pozitivní objekty, které je ale nutné manuálně eliminovat. Klíčová slova: letecké laserové skenování, detekce objektů mikroreliéfu, archeologický průzkum, digitální model reliéfu, local relief model, Krkonoše, Luční hora, Studniční horaThe thesis focuses on detection of micrographic features using airborne laser scanning data. The aim of the thesis is to propose a novel method for detecting terrain features and to identify and describe terrain objects corresponding to anthropogenic interventions in the arctic-alpine tundra region in the summit area of the Luční and Studniční hora mountains in the Krkonoše Mountains. Based on existing studies, a new detection method of specific micrographic features was proposed by combining two types of data, i.e. point cloud and its raster representation. Initially, polygon approximations of terrain objects were derived from a digital terrain model, and their spatial delineation was further refined locally using point clouds. The output provides the most complete and accurate polygon delineation of the terrain features to date, considering the available data, the specific characteristics of the area, and in comparison to previously conducted studies. The proposed algorithm has identified hundreds of additional terrain features compared to the reference data. However, this number includes false positive features, which need to be manually eliminated. Key words: airborne laser scanning, micrographic features detection, archaeological prospection, digital terrain model, local relief model, Krkonoše...Katedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyPřírodovědecká fakultaFaculty of Scienc