Digital Deforestation: Comparing Automated Approaches to the Production of Digital Terrain Models (DTMs) in Agisoft Metashape

This paper tests the suitability of automated point cloud classification tools provided by the popular image-based modeling (IBM) software package Agisoft Metashape for the generation of digital terrain models (DTMs) at moderately-vegetated archaeological sites. DTMs are often required for various forms of archaeological mapping and analysis. The suite of tools provided by Agisoft are relatively user-friendly as compared to many point cloud classification algorithms and do not require the use of additional software. Based on a case study from the Mycenaean site of Kastrouli, Greece, the mostly-automated, geometric classification tool “Classify Ground Points” provides the best results and produces a quality DTM that is sufficient for mapping and analysis. Each of the methods tested in this paper can likely be improved through manual editing of point cloud classification.

How Well Do We Know Europa’s Topography? An Evaluation of the Variability in Digital Terrain Models of Europa

Jupiter’s moon Europa harbors one of the most likely environments for extant extraterrestrial life. Determining whether Europa is truly habitable requires understanding the structure and thickness of its ice shell, including the existence of perched water or brines. Stereo-derived topography from images acquired by NASA Galileo’s Solid State Imager (SSI) of Europa are often used as a constraint on ice shell structure and heat flow, but the uncertainty in such topography has, to date, not been rigorously assessed. To evaluate the current uncertainty in Europa’s topography we generated and compared digital terrain models (DTMs) of Europa from SSI images using both the open-source Ames Stereo Pipeline (ASP) software and the commercial SOCET SET® software. After first describing the criteria for assessing stereo quality in detail, we qualitatively and quantitatively describe both the horizontal resolution and vertical precision of the DTMs. We find that the horizontal resolution of the SOCET SET® DTMs is typically 8–11× the root mean square (RMS) pixel scale of the images, whereas the resolution of the ASP DTMs is 9–13× the maximum pixel scale of the images. We calculate the RMS difference between the ASP and SOCET SET® DTMs as a proxy for the expected vertical precision (EP), which is a function of the matching accuracy and stereo geometry. We consistently find that the matching accuracy is ~0.5 pixels, which is larger than well-established “rules of thumb” that state that the matching accuracy is 0.2–0.3 pixels. The true EP is therefore ~1.7× larger than might otherwise be assumed. In most cases, DTM errors are approximately normally distributed, and errors that are several times the derived EP occur as expected. However, in two DTMs, larger errors (differences) occur and correlate with real topography. These differences primarily result from manual editing of the SOCET SET® DTMs. The product of the DTM error and the resolution is typically 4–8 pixel2 if calculated using the RMS image scale for SOCET SET® DTMs and the maximum images scale for the ASP DTMs, which is consistent with recent work using martian data sets and suggests that the relationship applies more broadly. We evaluate how ASP parameters affect DTM quality and find that using a smaller subpixel refinement kernel results in DTMs with smaller (better) resolution but, in some cases, larger gaps, which are sometimes reduced by increasing the size of the correlation kernel. We conclude that users of ASP should always systematically evaluate the choice of parameters for a given dataset.

Detection of earthflow dynamics using medium-resolution digital terrain models: Diachronic perspective of the Jovac earthflow, Southern Serbia

The paper presents and discusses the landslide research procedure related to the topography before and after its occurrence, using the comparative analysis of two medium-resolution digital terrain models. The case study is the Jovac mega-landslide—the largest landslide to occur in Serbia in the last 100 years, active for three days in February 1977. The indicators used to determine the volume and movement mechanism were the spatial distribution of elevation differences within the two digital terrain models (DTM), and the analysis of geomorphological features before the landslide. The obtained elevation differences allowed the definition of the approximate landslide volume: 11.6 × 106 m3. All the data obtained indicate that the movement mechanism falls into the category of earthflow.

A Methodological Framework for Free and Open-Source UAV-Based Archaeological Research

Full-coverage pedestrian survey to record cultural features on unexplored archaeological landscapes is costly in terms of time, money, and personnel. Over the past two decades, researchers have implemented remote sensing and landscape data collection techniques using unmanned aerial vehicles (UAVs) to combat some of these burdens, but the initial cost of equipment, software, and processing power has hindered the ubiquitous implementation of UAV technology as an accessible companion tool to traditional archaeological survey. This article presents a free and open-source, technology-independent analytical framework for the collection and processing of UAV images to produce high-resolution digital terrain models limited only by the equipment available to the researcher. Results from the free and open-source protocol are directly compared to those produced using proprietary software to illustrate the capabilities of freely available data processing tools for UAV-collected images. By replicating the methods outlined here, researchers should be able to identify and target areas of interest to increase fieldwork efficiency, decrease costs of implementing this technology, and produce high-resolution digital terrain models to conduct spatial analyses that pursue a deeper understanding of cultural landscapes.

IFCTERRAIN – A FREE AND OPEN SOURCE TOOL TO CONVERT DIGITAL TERRAIN MODELS (DTM) TO OPENBIM INDUSTRY FOUNDATION CLASSES (IFC)

Digital Terrain Models (DTM) play an important role for digital twins of the built environment. However, if the Building Information Modeling method (BIM) is used, many engineers find it difficult to provide BIM-compliant terrain models. We present a small tool with which classic DTM, which have been created by landsurveyors or geospatial engineers, can be converted into the format Industry Foundation Classes (IFC) in order to be used in openBIM projects. This paper first clarifies the use cases and then goes into detail on possible configurations of the transformation process. With the presented software tool IfcTerrain the user may select different export options concerning IFC object type of the terrain, geometric representation, georeferencing or the annotation with metadata. IfcTerrain is free and open source and was developed in the context of an educational institution.

Extraction of linear structures from digital terrain models using deep learning

This paper explores the role deep convolutional neural networks play in automated extraction of linear structures using semantic segmentation techniques in Digital Terrain Models (DTMs). DTM is a regularly gridded raster created from laser scanning point clouds and represents elevations of the bare earth surface with respect to a reference. Recent advances in Deep Learning (DL) have made it possible to explore the use of semantic segmentation for detection of terrain structures in DTMs. This research examines two novel and practical deep convolutional neural network architectures i.e. an encoder-decoder network named as SegNet and the recent state-of-the-art high-resolution network (HRNet). This paper initially focuses on the pixel-wise binary classification in order to validate the applicability of the proposed approaches. The networks are trained to distinguish between points belonging to linear structures and those belonging to background. In the second step, multi-class segmentation is carried out on the same DTM dataset. The model is trained to not only detect a linear feature, but also to categorize it as one of the classes: hollow ways, roads, forest paths, historical paths, and streams. Results of the experiment in addition to the quantitative and qualitative analysis show the applicability of deep neural networks for detection of terrain structures in DTMs. From the deep learning models utilized, HRNet gives better results.

Simulation of Images and Digital Terrain Models for the Mission BepiColombo

BepiColombo mission will provide Digital Terrain Models of the surface of Mercury by means of the stereo channel of the SIMBIO-SYS (Spectrometer and Imaging for MPO BepiColombo Integrated Observatory SYStem) imaging package onboard. The work here described presents a novel approach for the creation of higher resolution stereo products using the high-resolution channel of SIMBIO-SYS. Being the camera rigidly integrated with the spacecraft, this latter must be tilted to acquire stereo pairs necessary for the 3D reconstruction. A new method for image simulation and stereo reconstruction is presented in this work, where the input data are chosen as closely as possible to the real mission parameters. Different simulations are executed changing the illumination conditions and the stereo angles. The Digital Terrain Models obtained are evaluated and an analysis of the best acquisition conditions is performed, helping to improve the image acquisition strategy of BepiColombo mission. In addition, a strategy for the creation of a mosaic from different images acquired with the high-resolution channel of SIMBIO-SYS is explained, giving the possibility to obtain tridimensional products of extended targets.

Generative Adversarial Networks for automatic detection of mounds in Digital Terrain Models (Mars Arabia Terra)

<p>Mounds are positive relief features that can be ascribed to a variety of phenomena; they can be related to monogenic edifices due to spring or mud volcanism, rootless cones on top of lava flows, pingos and so on. In the case of sedimentary or spring case of mud extrusion, these mounds can be widespread regionally and/or contained in large complex craters, often in populations of several hundreds or thousands . Previous work on detection of such mounds in the Mars Arabia Terra involved exploiting morphometric parameters and mapping them onto Digital Terrain Models . In this work, we take a step further and develop more general methods to automatically detect them without explicitly defining the topographical features. We achieve this by using a generative framework trained in an adversarial fashion to produce realistic mappings with only a small number of training samples. Further, we introduce a terrain simulator based on this framework that learns the terrain simulation parameters, and allows us to induce domain specific knowledge automatically into the network.  Our key results indicate that learning latent representations based on simulations can offer improvements in detection accuracy, while making it more robust to changing terrain scenarios.</p><p><br><br></p>