Thesis advisor: Noah P. Snyder / Emerging methods for acquiring high-resolution topographic datasets have the potential to open new opportunities for quantitative geomorphic analysis. This study demonstrates a technique for rapidly obtaining structure from motion (SfM) photogrammetry-derived digital elevation models (DEMs) using aerial photographs acquired with a small unmanned aircraft system (sUAS). In conjunction with collection of aerial imagery, study sites are surveyed with a differential global position system (dGPS)-enabled total station (TPS) for georeferencing and accuracy assessment of sUAS SfM measurements. Results from sUAS SfM surveys of upland river channels in northern New England consistently produce DEMs and orthoimagery with ~1 cm pixel resolution. One-to-one point measurement comparisons demonstrate sUAS SfM systematically measures elevations about 0.16 ±0.23 m higher than TPS equivalents (0.28 m RMSE). Bathymetric (i.e. submerged or subaqueous) sUAS SfM measurements are 0.20 ±0.24 m (0.31 m RMSE) higher than TPS, whereas exposed (subaerial) points are 0.14 ±0.22 m (0.26 m RMSE) higher than TPS. Serial comparison of DEMs obtained before and after a two-year flood event indicates cut bank erosion and point bar deposition of ~0.10 m, consistent with expectations for channel evolution. DEMs acquired with the sUAS SfM are of comparable resolution but a lower cost alternative to those from airborne light detection and ranging (lidar), the current standard for topographic imagery. Furthermore, lidar is not available for much of the United States and sUAS SfM provides an efficient means for expanding coverage of this critical elevation dataset. Due to their utility in municipal, land use, and emergency planning, the demand for high-resolution topographic datasets continues to increase among governments, research institutions, and private sector consulting firms. Terrain analysis using sUAS SfM could therefore be a boon to river management and restoration in northern New England and other regions. / Thesis (MS) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Geology and Geophysics.

A classic Computer Vision task is the estimation of a 3D map from a collection of images. This thesis explores the online simultaneous estimation of camera poses and map points, often called Visual Simultaneous Localisation and Mapping [VSLAM]. In the near future the use of visual information by autonomous cars is likely, since driving is a vision dominated process. For example, VSLAM could be used to estimate the position of the car in relation to objects of interest, such as the road, other cars and pedestrians. Aimed at the creation of a real-time, robust, loop closing, single camera SLAM system, the properties of several state-of-the-art VSLAM systems and related techniques are studied. The system goals cover several important, if difficult, problems, which makes a solution widely applicable. This thesis makes two contributions: A rigorous qualitative analysis of VSLAM methods and a system designed accordingly. A novel tracking by matching scheme is proposed, which, unlike the trackers used by many similar systems, is able to deal better with forward camera motion. The system estimates general motion with loop closure in real time. The system is compared to a state-of-the-art monocular VSLAM algorithm and found to be similar in speed and performance.

Neste trabalho foi realizada a coleta de imagens de um trecho da área seca da UHE Álvaro de Souza Lima (Bariri – SP) e do lago da UNISINOS (São Leopoldo – RS), com câmeras e sensores, embarcados em RPAS (sistemas aéreos remotamente pilotados). As imagens resultantes foram processadas através da técnica estrutura do movimento (SfM), gerando ortomosaicos e modelos digitais de terrenos (MDTs). Pontos de checagem observados nestas imagens foram comparados com seus homólogos, obtidos por meio de posicionamento global diferencial no modo RTK. Testes estatísticos para verificação de hipóteses prévias de independência, aleatoriedade e normalidade, bem como, cálculos estatísticos e testes de inferência estatística foram realizados nas discrepâncias horizontais e verticais de ambos os produtos cartográficos. A verificação da exatidão vertical foi realizada somente nos produtos MDTs, a partir de feições de controle. O objetivo principal foi avaliar produtos cartográficos digitais gerados a partir de imagens coletadas por RPAS, quanto aos requisitos mínimos de exatidão geométrica para atendimento às leis estabelecidas pelos órgãos reguladores de energia elétrica. As discrepâncias horizontais observadas no ortomosaico da Unidade Hidroelétrica de Bariri apresentaram independência, não aleatoriedade e distribuição normal. Já para as discrepâncias verticais, foram independentes, aleatórias e normais. O teste t Student revelou tendência na posição horizontal e não tendência na vertical. O desvio padrão e o RMSE horizontais foram confrontados com o Padrão de Exatidão Cartográfica brasileiro através do teste Qui quadrado, indicando que os produtos têm precisão horizontal na escala 1:2000 (classe C) e, o MDT, na escala 1:10.000 (classe C), ambos com 95% de confiança e 22 e 19 graus de liberdade, respectivamente. A comparação entre MDTs conduziu a resultados semelhantes, porém demostrou que maiores discrepâncias ocorreram devido a fraca sobreposição de imagens. Para os produtos do lago UNISINOS, as discrepâncias cumpriram as hipóteses de independência e aleatoriedade. Porém, a normalidade não foi constatada pelo teste de Shapiro-Wilk. No entanto, através do teste U de Mann-Whitney, os produtos obtidos via sensor apresentaram semelhanças entre os pontos amostrados e seus homólogos. Através do teste direcional, os ângulos de direção foram medidos e caracterizaram a tendência horizontal para os ortomosaicos da UHE e do lago UNISINOS. Os resultados demonstraram que o mapeamento através de imagens via sensores embarcados em RPAS e processadas por SfM apresentaram exatidão posicional para integração com cartas de outras fontes, nas mesmas escalas. Portanto, estes produtos possibilitam a atualização de dados cartográficos para atender as demandas legais imputadas aos operadores de reservatórios. / In this work, the image collection was carried out on a stretch of dry area of the Alvaro de Souza Lima UHE (Bariri - SP) and of the UNISINOS lake (São Leopoldo) by cameras and sensors embedded in RPAS (Remotely Piloted Aerial System). The resulting images were processed using the Struture from Motion (SfM) technique in order to generate ortomosaicos and Digital Terrain Models (MDTs). Checkpoints observed in these images were compared with their counterparts obtained through differential global positioning in the RTK mode. Statistical tests for checking previous hypotheses of independence, randomness and normality, as well as statistical calculations and inference tests were conducted in the EN and h discrepancies of both cartographic products. The vertical accuracy verification was performed only in the MDTs products, from control features. The main objective of this work was to evaluate of digital cartographic products generated from photogrammetry collected by RPAS, with regard to the minimum requirements of geometric accuracy for fulfillment of the laws established by electric power regulatory agencies. The discrepancies observed in the orthomosaic of the Bariri Hydropower Plant exhibited independence, non randomness and normal distribution. However, for the MDT, these were independent, random, and normal. Students t-test showed a trend in the horizontal position and a non-trend in the vertical position. The horizontal standard deviation and RMSE (root mean square error) were analyzed according to the PEC-PCD Standard by the Chi-squared test, indicating that the orthomosaic fits at the 1:2,000 scale (class C), and the MDT, fits at the 1:10,000 scale (class C), both with a 95% confidence level and with degrees of freedom of 22 and 19, respectively. The comparison between MDTs leads to similar results, but showed that the largest discrepancies occur due to the weak overlap of the images. For the UNISINOS lake products, the discrepancies fulfilled the previous hypotheses of independence and randomness. But, the normality was not verified by the Shapiro-Wilk test. However, using the Mann-Whitney U test, the cartographic products obtained from a sensor showed similarities between the sampling points and their counterparts. By the directional test, it was measured the azimuth angles which characterizes the horizontal trend for the UHE and UNISINOS lake orthomosaics. The results demonstrated that the mapping by using images collected from sensors embedded in RPAS and processed through SfM presents positional accuracy for integration with maps from other sources, on the same scales. And therefore allows the updating of cartographic data map to meet the legal demands attributed to the reservoir operators.

Neste trabalho foi realizada a coleta de imagens de um trecho da área seca da UHE Álvaro de Souza Lima (Bariri – SP) e do lago da UNISINOS (São Leopoldo – RS), com câmeras e sensores, embarcados em RPAS (sistemas aéreos remotamente pilotados). As imagens resultantes foram processadas através da técnica estrutura do movimento (SfM), gerando ortomosaicos e modelos digitais de terrenos (MDTs). Pontos de checagem observados nestas imagens foram comparados com seus homólogos, obtidos por meio de posicionamento global diferencial no modo RTK. Testes estatísticos para verificação de hipóteses prévias de independência, aleatoriedade e normalidade, bem como, cálculos estatísticos e testes de inferência estatística foram realizados nas discrepâncias horizontais e verticais de ambos os produtos cartográficos. A verificação da exatidão vertical foi realizada somente nos produtos MDTs, a partir de feições de controle. O objetivo principal foi avaliar produtos cartográficos digitais gerados a partir de imagens coletadas por RPAS, quanto aos requisitos mínimos de exatidão geométrica para atendimento às leis estabelecidas pelos órgãos reguladores de energia elétrica. As discrepâncias horizontais observadas no ortomosaico da Unidade Hidroelétrica de Bariri apresentaram independência, não aleatoriedade e distribuição normal. Já para as discrepâncias verticais, foram independentes, aleatórias e normais. O teste t Student revelou tendência na posição horizontal e não tendência na vertical. O desvio padrão e o RMSE horizontais foram confrontados com o Padrão de Exatidão Cartográfica brasileiro através do teste Qui quadrado, indicando que os produtos têm precisão horizontal na escala 1:2000 (classe C) e, o MDT, na escala 1:10.000 (classe C), ambos com 95% de confiança e 22 e 19 graus de liberdade, respectivamente. A comparação entre MDTs conduziu a resultados semelhantes, porém demostrou que maiores discrepâncias ocorreram devido a fraca sobreposição de imagens. Para os produtos do lago UNISINOS, as discrepâncias cumpriram as hipóteses de independência e aleatoriedade. Porém, a normalidade não foi constatada pelo teste de Shapiro-Wilk. No entanto, através do teste U de Mann-Whitney, os produtos obtidos via sensor apresentaram semelhanças entre os pontos amostrados e seus homólogos. Através do teste direcional, os ângulos de direção foram medidos e caracterizaram a tendência horizontal para os ortomosaicos da UHE e do lago UNISINOS. Os resultados demonstraram que o mapeamento através de imagens via sensores embarcados em RPAS e processadas por SfM apresentaram exatidão posicional para integração com cartas de outras fontes, nas mesmas escalas. Portanto, estes produtos possibilitam a atualização de dados cartográficos para atender as demandas legais imputadas aos operadores de reservatórios. / In this work, the image collection was carried out on a stretch of dry area of the Alvaro de Souza Lima UHE (Bariri - SP) and of the UNISINOS lake (São Leopoldo) by cameras and sensors embedded in RPAS (Remotely Piloted Aerial System). The resulting images were processed using the Struture from Motion (SfM) technique in order to generate ortomosaicos and Digital Terrain Models (MDTs). Checkpoints observed in these images were compared with their counterparts obtained through differential global positioning in the RTK mode. Statistical tests for checking previous hypotheses of independence, randomness and normality, as well as statistical calculations and inference tests were conducted in the EN and h discrepancies of both cartographic products. The vertical accuracy verification was performed only in the MDTs products, from control features. The main objective of this work was to evaluate of digital cartographic products generated from photogrammetry collected by RPAS, with regard to the minimum requirements of geometric accuracy for fulfillment of the laws established by electric power regulatory agencies. The discrepancies observed in the orthomosaic of the Bariri Hydropower Plant exhibited independence, non randomness and normal distribution. However, for the MDT, these were independent, random, and normal. Students t-test showed a trend in the horizontal position and a non-trend in the vertical position. The horizontal standard deviation and RMSE (root mean square error) were analyzed according to the PEC-PCD Standard by the Chi-squared test, indicating that the orthomosaic fits at the 1:2,000 scale (class C), and the MDT, fits at the 1:10,000 scale (class C), both with a 95% confidence level and with degrees of freedom of 22 and 19, respectively. The comparison between MDTs leads to similar results, but showed that the largest discrepancies occur due to the weak overlap of the images. For the UNISINOS lake products, the discrepancies fulfilled the previous hypotheses of independence and randomness. But, the normality was not verified by the Shapiro-Wilk test. However, using the Mann-Whitney U test, the cartographic products obtained from a sensor showed similarities between the sampling points and their counterparts. By the directional test, it was measured the azimuth angles which characterizes the horizontal trend for the UHE and UNISINOS lake orthomosaics. The results demonstrated that the mapping by using images collected from sensors embedded in RPAS and processed through SfM presents positional accuracy for integration with maps from other sources, on the same scales. And therefore allows the updating of cartographic data map to meet the legal demands attributed to the reservoir operators.

Fler och fler industrier vänder blickarna mot A.I. (artificiell intelligens) för att undersöka om och hur det kan användas för att effektivisera olika processer. Men för att träna upp en A.I. krävs oftast stora mängder data där man kan behöva förbereda väldigt mycket manuellt innan man ens kan påbörja träningsprocessen. SCA Skog AB ser dock många fördelar med att göra A.I. till en naturlig del av sin digitaliseringsprocess, där man bland annat är intresserad utav visuella bedömningar av träd. Dataset för visuella bedömningar kan se ut på olika sätt, men i detta fall var det relevant att skapa dataset i form av konturer för trädstammar. Med hjälp av en A.I. som skulle kunna visuellt segmentera och klassificera träd så skulle man öppna upp för många nya möjligheter inom skogsindustrin. Under detta projekt har jag undersökt hur man skulle kunna automatisera processen för skapandet av dataset i  skogsmiljöer för just visuella bedömningar. Som ett resultat av att försöka uppnå detta, så fick jag experimentera med bildbaserade punktmoln som på olika sätt tillät projektet att avancera framåt. Ur dessa punktmoln kunde jag sedan segmentera träden för att i nästa process skapa konturer längs alla träd med hjälp av utvunnen data ur segmenteringen. Jag tittade först och främst på hur man automatiskt skulle kunna skapa konturer för alla träd i bildsekvensen, för att sedan låta en användare gå in och finjustera konturerna. I resultatet kan man sedan tydligt se skillnaden i tidsåtgång för att använda programmet och inte. Programmet kan skapa och uppdatera pixel-masker snabbare än vad jag manuellt kunde utföra samma arbete, där jag dock hade önskat på en mer markant skillnad i tidsåtgång jämfört med den rent manuella insatsen. Under projektets gång så kunde jag identifiera några större problem som förhindrade detta, där man med lämplig utrustning skulle kunna uppnå ett mycket bättre resultat än vad som gjordes under detta projekt. Resultaten talar ändå för att det kan vara lönt att undersöka metoden mer ingående. / More and more industries are turning their eyes towards A.I. (artificial intelligence) and its rapid development, in hope of utilizing it to remove labor intense operations. But large amounts of manually processed data is often required before starting the learning process, which can be a huge problem to deal with. SCA Skog AB is still very curious in how they could use A.I. in forestry, where visual inspection of trees is of particular interest. There are many visual problems that modern A.I. can solve, where in this case it’s a matter of finding contours of trees and classify them. If this would be possible, a lot of interesting opportunities would open up to be experimented with. During this project I’ve examined the possibility of reducing the time it takes to manually create datasets of forest environments for this particular visual problem. As a result of trying to achieve this, I had to examine image-based point clouds and their properties to find out how they could be used in this process. From the SfM-point cloud I was able to segment all visible trees with an segmentation algorithm and isolate these points to extract the 2D→3Dconnection. I could then use that connection to create pixel masks and apply it to the image sequence to paint out all the contours of the segmented trees. A method to automatically update these pixel masks in terms of adding and removal was also implemented, where any update would propagate through the image sequence and reduce the time for manual adjustment. From testing the program, it’s clear that time could be saved doing various kinds of contour updating-operations. The program could by itself create pixel masks that then could be updated in a way that a lot of need for manual updating was reduced, though the result in terms of time saved was not as substantial as one would have hoped for. Issues with the point cloud caused some major  problems due to it’s low precision. Using better equipment for image gathering would most likely be the best way to improve the results of this project. The result still tells us that this method is worth researching further.

Deep Learning is dominant in the field of computer vision, thanks to its high performance. This high performance is driven by large annotated datasets and proper evaluation benchmarks. However, two important areas in computer vision, depth-based hand segmentation, and local features, respectively lack a large well-annotated dataset and a benchmark protocol that properly demonstrates its practical performance. Therefore, in this thesis, we focus on these two problems. For hand segmentation, we create a novel systematic way to easily create automatic semantic segmentation annotations for large datasets. We achieved this with the help of traditional computer vision techniques and minimal hardware setup of one RGB-D camera and two distinctly colored skin-tight gloves. Our method allows easy creation of large-scale datasets with high annotation quality. For local features, we create a new modern benchmark, that reveals their different aspects. Specifically wide-baseline stereo matching and Multi-View Stereo (MVS), of keypoints in a more practical setup, namely Structure-from-Motion (SfM). We believe that through our new benchmark, we will be able to spur research on learned local features to a more practical direction. In this respect, the benchmark developed for the thesis will be used to host a challenge on local features. / Graduate

The purpose of this study is comparing elevation models from Terrestrial laser scanner (TLS) and Unmanned aerial system (UAS) photogrammetry focusing on detecting microtopography and the relationship between elevation differences and image textures. The soils on agricultural lands are permanently modified by intensive farming activities almost every year. The microtopography of the soil, that plays an important role in the surface runoff and infiltration, depends on cultivation practices and the field environment. By way of example: crop residues, furrows, tillage direction, and slope may impact the soil nutrient and erosion. To better understand and prevent soil degradation via erosion, 3-D reconstructions of high-resolution soil monitoring are required. In this study, we try to circumnavigate the soil roughness associated with sustainable practices and physical characteristics of fields by collecting soil datasets from non-contacted remote sensing platforms. The amount of soil roughness was observed environmental conditions derived from the Terrestrial Laser Scanner (TLS) and the Unmanned Aerial System (UAS) photogrammetry within harvested fields in Eastern Central Iowa. Additionally, by focusing on local relief detections and the relationship between outlier distributions and image textures, the two datasets were compared. Both TLS and UAS derived point clouds successfully reconstructed digital elevation models ~ 5cm RMSE after the registration and merge process, and these models showed local reliefs of study areas with fine details. However, several outlier cluster points were detected in the comparisons between TLS and UAS derived DEMs. To discover the outlier distributions, image texture was addressed with global and local block analysis. Since there were no significant correlations, most of the study sites show that poor texture of ground may trigger high elevation errors. To enhance the texture of images, several possible solutions are described, such as local contrast enhancement using the Wallis filter.

<p>Pavement macrotexture contributes greatly to road surface friction, which in turn plays a significant role in reducing road incidents. Conventional methods for macrotexture measurement techniques (e.g., the sand patch method, the outflow method, and laser measuring) are either expensive, time-consuming, or of poor repeatability. This thesis aims to develop and evaluate affordable and convenient alternative approaches to determine pavement macrotexture. The proposed solution is based on multi-view smartphone images collected in situ over the pavement. Computer vision techniques are then applied to create high resolution three-dimensional (3D) models of the pavement. The thesis develops the analytics to determine two primary macrotexture metrics: mean profile depth and aggregation loss. Experiments with 790 images over 25 spots of three State Roads and 6 spots of the INDOT test site demonstrated that the image-based method can yield reliable results comparable to conventional laser texture scanner results. Moreover, based on experiments with 280 images over 7 sample plates with different aggregate loss percentage, the newly developed analytics were proven to enable estimation of the aggregation loss, which is largely compromised in the laser scanning technique and conventional MPD calculation approach. The root mean square height based on the captured images was verified in this thesis as a more comprehensive metric for macrotexture evaluation. It is expected that the developed approach and analytics can be adopted for practical use at a large scale. </p>

Yes / Designed as a pragmatic approach that anticipates change to cultural heritage, this chapter discusses responses that encompass records for tangible cultural heritage (monuments, sites and landscapes) and the narratives that see the impact upon them. The Curious Travellers project provides a mechanism for digitally documenting heritage sites that have been destroyed or are under immediate threat from unsympathetic development, neglect, natural disasters, conflict and cultural vandalism. The project created and tested data-mining and crowd-sourced workflows that enable the accurate digital documentation and 3D visualisation of buildings, archaeological sites, monuments and heritage at risk. When combined with donated content, image data are used to recreate 3D models of endangered and lost monuments and heritage sites using a combination of open-source and proprietary methods. These models are queried against contextual information, helping to place and interrogate structures with relevant site and landscape data for the surrounding environment. Geospatial records such as aerial imagery and 3D mobile mapping laser scan data serve as a framework for adding new content and testing accuracy. In preserving time-event records, image metadata offers important information on visitor habits and conservation pressures, which can be used to inform measures for site management. / The Curious Travellers project was funded as a component of the AHRC Digital Transformations Theme Large Grant ‘Fragmented Heritage’ (AH/L00688X/1). AHRC Follow-on funding has seen this approach contribute to the BReaTHe project (AH/S005951/1) which seeks to Build Resilience Through Heritage for displaced communities and with a contribution to the BA Cities and Infrastructures Scheme project, ‘Reducing Disaster Risk to Life and Livelihoods by evaluating the seismic performance of retrofitted interventions within Kathmandu’s UNESCO World Heritage Site during the 2015 Earthquake’, with Durham University (KF1\100109).

Soil erosion is a complex geomorphological process with varying influences of different impacts at different spatio-temporal scales. To date, measurement of soil erosion is predominantly realisable at specific scales, thereby detecting separate processes, e.g. interrill erosion contrary to rill erosion. It is difficult to survey soil surface changes at larger areal coverage such as field scale with high spatial resolution. Either net changes at the system outlet or remaining traces after the erosional event are usually measured. Thus, either quasi-point measurements are extrapolated to the corresponding area without knowing the actual sediment source as well as sediment storage behaviour on the plot or erosion rates are estimated disrupting the area of investigation during the data acquisition impeding multi-temporal assessment. Furthermore, established methods of soil erosion detection and quantification are typically only reliable for large event magnitudes, very labour and time intense, or inflexible. To better observe soil erosion processes at field scale and under natural conditions, the development of a method is necessary, which identifies and quantifies sediment sources and sinks at the hillslope with high spatial resolution and captures single precipitation events as well as allows for longer observation periods. Therefore, an approach is introduced, which measures soil surface changes for multi-spatio-temporal scales without disturbing the area of interest. Recent advances regarding techniques to capture high resolution topography (HiRT) data led to several promising tools for soil erosion measurement with corresponding advantages but also disadvantages. The necessity exists to evaluate those methods because they have been rarely utilised in soil surface studies. On the one hand, there is terrestrial laser scanning (TLS), which comprises high error reliability and retrieves 3D information directly. And on the other hand, there is unmanned aerial vehicle (UAV) technology in combination with structure from motion (SfM) algorithms resulting in UAV photogrammetry, which is very flexible in the field and depicts a beneficial perspective. Evaluation of the TLS feasibility reveals that this method implies a systematic error that is distance-related and temporal constant for the investigated device and can be corrected transferring calibration values retrieved from an estimated lookup table. However, TLS still reaches its application limits quickly due to an unfavourable (almost horizontal) scanning view at the soil surface resulting in a fast decrease of point density and increase of noise with increasing distance from the device. UAV photogrammetry allows for a better perspective (birds-eye view) onto the area of interest, but possesses more complex error behaviour, especially in regard to the systematic error of a DEM dome, which depends on the method for 3D reconstruction from 2D images (i.e. options for additional implementation of observations) and on the image network configuration (i.e. parallel-axes and control point configuration). Therefore, a procedure is developed that enables flexible usage of different cameras and software tools without the need of additional information or specific camera orientations and yet avoiding this dome error. Furthermore, the accuracy potential of UAV photogrammetry describing rough soil surfaces is assessed because so far corresponding data is missing. Both HiRT methods are used for multi-temporal measurement of soil erosion processes resulting in surface changes of low magnitudes, i.e. rill and especially interrill erosion. Thus, a reference with high accuracy and stability is a requirement. A local reference system with sub-cm and at its best 1 mm accuracy is setup and confirmed by control surveys. TLS and UAV photogrammetry data registration with these targets ensures that errors due to referencing are of minimal impact. Analysis of the multi-temporal performance of both HiRT methods affirms TLS to be suitable for the detection of erosion forms of larger magnitudes because of a level of detection (LoD) of 1.5 cm. UAV photogrammetry enables the quantification of even lower magnitude changes (LoD of 1 cm) and a reliable observation of the change of surface roughness, which is important for runoff processes, at field plots due to high spatial resolution (1 cm²). Synergetic data fusion as a subsequent post-processing step is necessary to exploit the advantages of both HiRT methods and potentially further increase the LoD. The unprecedented high level of information entails the need for automatic geomorphic feature extraction due to the large amount of novel content. Therefore, a method is developed, which allows for accurate rill extraction and rill parameter calculation with high resolution enabling new perspectives onto rill erosion that has not been possible before due to labour and area access limits. Erosion volume and cross sections are calculated for each rill revealing a dominant rill deepening. Furthermore, rill shifting in dependence of the rill orientation towards the dominant wind direction is revealed. Two field plots are installed at erosion prone positions in the Mediterranean (1,000 m²) and in the European loess belt (600 m²) to ensure the detection of surface changes, permitting the evaluation of the feasibility, potential and limits of TLS and UAV photogrammetry in soil erosion studies. Observations are made regarding sediment connectivity at the hillslope scale. Both HiRT methods enable the identification of local sediment sources and sinks, but still exhibiting some degree of uncertainty due to the comparable high LoD in regard to laminar accumulation and interrill erosion processes. At both field sites wheel tracks and erosion rills increase hydrological and sedimentological connectivity. However, at the Mediterranean field plot especially dis-connectivity is obvious. At the European loess belt case study a triggering event could be captured, which led to high erosion rates due to high soil moisture contents and yet further erosion increase due to rill amplification after rill incision. Estimated soil erosion rates range between 2.6 tha-1 and 121.5 tha-1 for single precipitation events and illustrate a large variability due to very different site specifications, although both case studies are located in fragile landscapes. However, the susceptibility to soil erosion has different primary causes, i.e. torrential precipitation at the Mediterranean site and high soil erodibility at the European loess belt site. The future capability of the HiRT methods is their potential to be applicable at yet larger scales. Hence, investigations of the importance of gullys for sediment connectivity between hillslopes and channels are possible as well as the possible explanation of different erosion rates observed at hillslope and at catchment scales because local sediment sink and sources can be quantified. In addition, HiRT data can be a great tool for calibrating, validating and enhancing soil erosion models due to the unprecedented level of detail and the flexible multi-spatio-temporal application.