Prospecting of Architectural Features Using LiDAR‐UAV Technology, Deep Neural Networks and Visualization Techniques: A Case Study in Kuélap and Cambolín (NW Peru)

Archaeology has been profoundly transformed by the advent of airborne laser scanning (ALS) technology (a.k.a airborne LiDAR). High-resolution and high-precision synoptic views of earth’s topography are now available, even in densely forested environments, to identify and characterize landform patterns resulting from past human occupation. ALS-based archaeological prospection relies on digital terrain model (DTM) visualization techniques (VTs) that highlight subtle topographical changes perceived and interpreted by archaeologists. An increasing number of VTs have been developed, and they have been evaluated to date mainly based on subjective human perception. This study developed a new approach based on state-of-the-art computer-vision algorithms to benchmark VTs using objective metrics. Thirteen VTs were applied to a ALS-derived DTM, and a deep convolution neural network (deep CNN) was implemented and trained to automatically detect and segment archaeological structures from these images. Visual interpretation of the images showed that the most informative VT was e2MSTP, which combined a multiscale topographic analysis (MSTP) with a morphologically explicit image and a slope-invariant relief detrending technique. The deep CNN approach confirmed these results and provided objective performance metrics. This study indicates that the computer vision approach opens new perspectives in the objective selection of the most suitable VT for archaeological prospection.

Monitoring gully change: A comparison of airborne and terrestrial laser scanning using a case study from Aratula, Queensland

Terrestrial laser scanning improves digital elevation models and topsoil pH modelling in regions with complex topography and dense vegetation

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Very few studies have been devoted to understanding the digital terrain model (DTM) creation for Amazon forests. DTM has a special and important role when airborne laser scanning is used to estimate vegetation biomass. We examined the influence of pulse density, spatial resolution, filter algorithms, vegetation density and slope on the DTM quality. Three Amazonian forested areas were surveyed with airborne laser scanning, and each original point cloud was reduced targeting to 20, 15, 10, 8, 6, 4, 2, 1, 0.75, 0.5 and 0.25 pulses per square meter based on a random resampling process. The DTM from resampled clouds was compared with the reference DTM produced from the original LiDAR data by calculating the deviation pixel by pixel and summarizing it through the root mean square error (RMSE). The DTM from resampled clouds were also evaluated considering the level of agreement with the reference DTM. Our study showed a clear trade-off between the return density and the horizontal resolution. Higher forest canopy density demanded higher return density or lower DTM resolution.

Digital terrain models (DTM) based on airborne laser scanning (ALS) are an important source for identifying and monitoring archaeological sites and landscapes. However, a DTM is only one of many representations of a given surface. Its accuracy and quality must conform to its purpose and are a result of several considerations and decisions along the processing chain. One of the most important factors of ALS-based DTM generation is ground point filtering, i.e., the classification of the acquired point-cloud into terrain and off-terrain points. Filtering is not straightforward. The resulting DTM is usually a compromise that might show the surface below very dense vegetation while losing detail in other areas. In this paper, we show that in very complex situations (e.g., strongly varying vegetation cover), an optimal compromise is difficult to achieve, and more than one filter with different settings adapted to the varying degree of vegetation cover is necessary. For practical reasons, the results need to be combined into a single DTM. This is demonstrated using the case study of a Mediterranean landscape in Croatia, which consists of open areas (agricultural and grassland), olive plantations, as well as extremely dense and evergreen macchia vegetation. The results are the first step toward an adaptive ground point filtering strategy that might be useful far beyond the field of archaeology.

: Airborne laser scanning (ALS) is an active remote se nsing technique that can provide 3D information about the scene. Over the last few years, ALS has proved its capa bility of rapid acquisition of accurate topographic data. With the advantages of high accuracy, ALS data has become an accepted data source for highly automated acquisition of digital surface models (DSM) as well as for the generation of digital elevation models (DEM ) which have been used in many applications such as civic planni ng, military navigation and natural hazard risk assessmen t. To utilize the 3D data provided by the ALS systems however, efficient methods for data processing and model construction needs to be developed. In this paper some recent technique on analysis and visualization modelling of ALS data will be presented. After the brief presentation of the ALS system, the computation of ALS data is introduced. The data processing chain for producing DSM is outlined. A data filtering method is pro posed for generating DEM. A 3D visualization modelling study on ground scene is carried out and the framework of establishing visualization of DSM and DEM using ALS data is brought as well. Key words : airborne laser scanning; digital elevation models; visualization modeling; morphological filter

Structure of the floodplain, especially its topography and vegetation, influences the overland flow and dynamics of floods which are key factors shaping ecosystems in surface water-fed wetlands. Therefore elaboration of the digital terrain model (DTM) of a high spatial accu racy is crucial in hydrodynamic flow m odelling in river valleys. In this study the research was conducted in the unique Central European complex of fens and marshes - the Lower Biebrza river valley. The area is represented mainly by peat ecosystems which according to EU Water Framework Directive (WFD) are called “water -dependent ecosystems”. Development of accurate DT M in these areas which are overgrown by dense wetland vegetation consisting of alder forest, willow shrubs, reed, sedges and grass is very difficult, therefore to represent terrain in high accuracy the airborne laser sc anning data (ALS) with scan ning density of 4 points/m 2 was used and the correction of the “vegetation effect” on DTM was ex ecuted. This correction was performed utilizing remotely sensed images, topographical survey using the Real Time Kinematic positioning and vegetation height measurements. In order to classify different types of vegetation within research area the object based image analysis (OBIA) was used. OBIA allowed partitioning remotely sensed imagery into meaningful image-objects, and assessing their characteristics through spatial and spectral scale. The final maps of vegetation patches that include attributes of vegetation height and vegetation spectral properties, utilized both the laser scanni ng data and the vegetation indices developed on the basis of airborne and satellite imagery. This data was used in process of segmentation, attribution and classification. Several different vegetation indices were tested to distinguish different types of vegetation in wetland area. The OBIA classification allowed correction of the “vegetation effect” on DTM. The final di gital terrain model was compared and examined within distinguished land cover classes (formed mainly by natural vegetation of the river valley) with archival height models developed through interpolation of ground points measured with GPS RTK and also with elevation models from the ASTER-GDEM and SRTM programs. The resear ch presented in this paper allowed improving quality of hydrodynamic modelling in the surface water-fed wetlands protected within Biebrza Na tional Park. Additionally, the comparison with other digital terrain models allowed to dem onstrate the importance of accurate topography products in such modelling. The ALS data also significantly improved the accuracy and actuality of the river Biebrza course, its tributaries and location of numerous oxbows typical in this part of the river valley in comparison to previously available data. This type of data also helped to refine the river valle y cross-sections, designate river banks and to develop the slope map of the research area. Keywords: Airborne Laser Scanning (ALS), Object Based Image Analysis (OBIA), Digital Terrain Model (DTM), Biebrza, hydrology

Combining airborne and terrestrial laser scanning for quantifying erosion and deposition by a debris flow event

Alpine topography is formed by a complex series of geomorphological processes that result in a vast number of different landforms. The youngest and most diverse landforms are various Quaternary sedimentary bodies, each characterised by its unique landform features. The formation of Quaternary sedimentary bodies and their features derive from the dominant building sedimentary processes. In recent years, studies of Quaternary sedimentary bodies and processes have been greatly aided by the use of digital elevation models (DEMs) derived by airborne laser scanning (ALS). High-resolution DEMs allow detailed mapping of sedimentary bodies, detection of surface changes, and recognition of the building sedimentary processes. DEMs are often displayed as hillshaded reliefs, the most common visualisation technique, which suffers from the limitation of a single illumination source. As a result, features can be barely visible or even invisible to the viewer if they are parallel to the light source or hidden in the shadow. These limitations become challenging when representing landforms and subtle landscape features in a diverse alpine topography. In this study, we focus on eleven visualisations of Quaternary sedimentary bodies and their sedimentary and morphological features on a 0.5 m resolution DEM. We qualitatively compare analytical hillshading with a set of visualisation techniques contained in the Raster Visualisation Toolbox software, primarily hillshading from multiple directions RGB, 8-bit sky view factor and 8-bit slope. The aim is to determine which visualisation technique is best suited for visual recognition of sedimentary bodies and sedimentation processes in complex alpine landscapes. Detailed visual examination of previously documented Pleistocene moraine and lacustrine deposits, Holocene alluvial fans, scree deposits, debris flow and fluvial deposits on the created visualisations revealed several small-scale morphological and sedimentary features that were previously difficult or impossible to detect on analytical hillshading and aerial photographs. Hillshading from multiple directions resulted in a visualisation that could be universally applied across the mountainous and hilly terrains. In contrast, 8-bit sky view factor and 8-bit slope visualisations created better visibility and facilitated interpretation of subtle and small-scale (less than ten metres) sedimentary and morphological features.

Abstract. Aerial photography was, for decades, an invaluable tool for archaeological prospection, in spite of the limitation of this method to deforested areas. The airborne laser scanning (ALS) method can be nowadays used to map complex areas and suitable complement earlier findings. This article describes visualization and image processing methods that can be applied on digital terrain models (DTMs) to highlight objects hidden in the landscape. Thanks to the analysis of visualized DTM it is possible to understand the landscape evolution including the differentiation between natural processes and human interventions. Different visualization methods were applied on a case study area. A system of parallel tracks hidden in a forest and its surroundings – part of old route called "Devil's Furrow" near the town of Sázava was chosen. The whole area around well known part of Devil's Furrow has not been prospected systematically yet. The data from the airborne laser scanning acquired by the Czech Office for Surveying, Mapping and Cadastre was used. The average density of the point cloud was approximately 1 point/m2 The goal of the project was to visualize the utmost smallest terrain discontinuities, e.g. tracks and erosion furrows, which some were not wholly preserved. Generally we were interested in objects that are clearly not visible in DTMs displayed in the form of shaded relief. Some of the typical visualization methods were tested (shaded relief, aspect and slope image). To get better results we applied image-processing methods that were successfully used on aerial photographs or hyperspectral images in the past. The usage of different visualization techniques on one site allowed us to verify the natural character of the southern part of Devil’s Furrow and find formations up to now hidden in the forests.

In the German Authoritative Topographic Cartographic Information System (ATKIS), the 2D positions and the heights of objects such as roads are stored separately in the digital landscape model (DLM) and digital terrain model (DTM), which is often acquired by airborne laser scanning (ALS). However, an increasing number of applications require a combined processing and visualization of these two data sets. Due to different kinds of acquisition, processing, and modelling discrepancies exist between the DTM and DLM and thus a simple integration may lead to semantically incorrect 3D objects. For example, roads may be situated on strongly tilted DTM parts and rivers sometimes flow uphill. In this paper we propose an algorithm for the adaptation of 2D road centrelines to ALS data by means of network snakes. Generally, the image energy for the snakes is defined based on ALS intensity and height information and derived products. Additionally, buildings and bridges as strong features in height data are exploited in order to support the road adaptation process. Extracted buildings as priors modified by a distance transform are used to create a force of repulsion for the road vectors integrated in the image energy. In contrast, bridges give strong evidence for the correct road position in the height data. Therefore, the image energy is adapted for the bridge points. For that purpose bridge detection in the DTM is performed starting from an approximate position using template matching. Examples are given which apply the concept of network-snakes with new image energy for the adaptation of road networks to ALS data taking advantage of the prior known topology.

High resolution digital elevation models (DEMs) have been used in many applications such as civic planning, military navigation and natural hazard risk assessment. DEMs can be derived using various remote sensing techniques, such as photogrammetry, radar interferometry (InSAR), airborne laser scanning (ALS) and high resolution space imaging. This paper refines the methods of height estimation over diverse terrain using a combination of ALS and InSAR data, including Shuttle Radar Topography Mission (SRTM) and repeat-pass InSAR DEMs. This study aims to generate fine resolution DEMs from ALS and InSAR data for the study area. The variable topography of the region and proximal urban interface provides a good test bed for DEM generation techniques. The derived DEMs are examined using ground survey data with the aid of GIS. Keyword: ERS-1/2, tandem mission, InSAR, DEM, ALS

Full-waveform Airborne Laser Scanning for the detection of medieval archaeological microtopographic relief

Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.