Evaluating Image Normalization via GANs for Environmental Mapping: A Case Study of Lichen Mapping Using High-Resolution Satellite Imagery

Illumination variations in non-atmospherically corrected high-resolution satellite (HRS) images acquired at different dates/times/locations pose a major challenge for large-area environmental mapping and monitoring. This problem is exacerbated in cases where a classification model is trained only on one image (and often limited training data) but applied to other scenes without collecting additional samples from these new images. In this research, by focusing on caribou lichen mapping, we evaluated the potential of using conditional Generative Adversarial Networks (cGANs) for the normalization of WorldView-2 (WV2) images of one area to a source WV2 image of another area on which a lichen detector model was trained. In this regard, we considered an extreme case where the classifier was not fine-tuned on the normalized images. We tested two main scenarios to normalize four target WV2 images to a source 50 cm pansharpened WV2 image: (1) normalizing based only on the WV2 panchromatic band, and (2) normalizing based on the WV2 panchromatic band and Sentinel-2 surface reflectance (SR) imagery. Our experiments showed that normalizing even based only on the WV2 panchromatic band led to a significant lichen-detection accuracy improvement compared to the use of original pansharpened target images. However, we found that conditioning the cGAN on both the WV2 panchromatic band and auxiliary information (in this case, Sentinel-2 SR imagery) further improved normalization and the subsequent classification results due to adding a more invariant source of information. Our experiments showed that, using only the panchromatic band, F1-score values ranged from 54% to 88%, while using the fused panchromatic and SR, F1-score values ranged from 75% to 91%.

Automatic vectorization of rectangular manmade objects: a case study applying OpenCV and GDAL on UAV imagery

UAV imagery has a big role in environmental mapping: various indices regarding plant health, soil condition or geological objects can be determined, or 3D models can be built for accurate measurements. Automatic vectorization of satellite images is widely applied nowadays for land coverage determination purposes. However, larger resolution UAV images are hard to process following this theory: too many details result in a long computing time. We propose a FOSS (free and open-source software) analytical solution for detecting and vectorizing quasi-rectangular shaped (mainly manmade) objects on relatively high-resolution images. Our sample area is the cemetery and its surroundings in Istenmezeje, Heves County, Hungary. The graves are good examples of regular, rectangular manmade objects. The traditional cadastral mapping of these sites means a large amount of digitizing work. We have used Python environment for conducting image analysis: delineating and vectorizing the grave outlines for the large-scale mapping of the cemetery. Open-source programming libraries were used during the process: OpenCV and GDAL/OGR. With these tools, we were able to digitize the graves automatically with systematic errors. Approximately 70–80 of 100 graves were correctly recognised (their number varies depending on the adjustable variables: the size and detailedness of the contours to be detected). Our approach is a relatively new methodology in large-scale cartography: computer vision tools have not been used widely for mapmaking purposes. The development of artificial intelligence and open-source tools connected to it may contribute to the broader dissemination of similar methodologies in cartography and GIS.

ISO CLUSTER CLASSIFIER BY ARCGIS FOR UNSUPERVISED CLASSIFICATION OF THE LANDSAT TM IMAGE OF REYKJAVÍK

The paper presents the use of the Landsat TM image processed by the ArcGIS Spatial Analyst Tool for environmental mapping of southwestern Iceland, region of Reykjavik. Iceland is one of the most special Arctic regions with unique flora and landscapes. Its environment is presented by vulnerable ecosystems of highlands where vegetation is affected by climate, human or geologic factors: overgrazing, volcanism, annual temperature change. Therefore, mapping land cover types in Iceland contribute to the nature conservation, sustainable development and environmental monitoring purposes. This paper starts by review of the current trends in remote sensing, the importance of Landsat TM imagery for environmental mapping in general and Iceland in particular, and the requirements of GIS specifically for satellite image analysis. This is followed by the extended methodological workflow supported by illustrative print screens and technical description of data processing in ArcGIS. The data used in this research include Landsat TM image which was captured using GloVis and processed in ArcGIS. The methodology includes a workflow involving several technical steps of raster data processing in ArcGIS: 1) coordinate projecting, 2) panchromatic sharpening, 3) inspection of raster statistics, 4) spectral bands combination, 5) calculations, 6) unsupervised classification, 7) mapping. The classification was done by clustering technique using ISO Cluster algorithm and Maximum Likelihood Classification. This paper finally presents the results of the ISO Cluster application for Landsat TM image processing and concludes final remarks on the perspectives of environmental mapping based on Landsat TM image processing in ArcGIS.The results of the classification present landscapes divided into eight distinct land cover classes: 1) bare soils; 2) shrubs and smaller trees in the river valleys, urban areas including green spaces; 3) water areas; 4) forests including the Reykjanesfólkvangur National reserve; 5) ice-covered areas, glaciers and cloudy regions; 6) ravine valleys with a sparse type of the vegetation: rowan, alder, heathland, wetland; 7) rocks; 8) mixed areas. The final remarks include the discussion on the development of machine learning methods and opportunities of their technical applications in GIS-based analysis and Earth Observation data processing in ArcGIS, including image analysis and classification, mapping and visualization, machine learning and environmental applications for decision making in forestry and sustainable development.

AUTOMATED AND PERMANENT LONG-RANGE TERRESTRIAL LASER SCANNING IN A HIGH MOUNTAIN ENVIRONMENT: SETUP AND FIRST RESULTS

A terrestrial laser scanner (TLS) of the type RIEGL VZ-6000 has been permanently installed and automated at Hintereisferner glacier located in the Ötztal Alps, Austria, to identify snow (re)distribution from surface height changes. A first case study is presented that shows and discusses detected snow distribution at the glacier after a snowfall event, together with concurrent snow erosion and deposition caused by avalanches. The paper shows the potential of a TLS system in a high mountain environment, which is also applicable to other environmental mapping applications. It introduces the setup of the TLS system, its automation procedure, and a first and preliminary uncertainty analysis. TLS data are generally influenced by four uncertainty sources: atmospheric conditions, scanning geometry, mechanical properties, and surface reflectance properties. The first three sources have significant influence on the TLS data at Hintereisferner, whereby the total accuracy of the TLS system is estimated to be in a range of a few decimetres, subject to ongoing more detailed data analysis.

Lab-Based Evaluation of Device-Free Passive Localization Using Multipath Channel Information

The interconnection of devices, driven by the Internet of Things (IoT), enables a broad variety of smart applications and location-based services. The latter is often realized via transponder based approaches, which actively determine device positions within Wireless Sensor Networks (WSN). In addition, interpreting wireless signal measurements also enables the utilization of radar-like passive localization of objects, further enhancing the capabilities of WSN ranging from environmental mapping to multipath detection. For these approaches, the target objects are not required to hold any device nor to actively participate in the localization process. Instead, the signal delays caused by reflections at objects within the propagation environment are used to localize the object. In this work, we used Ultra-Wide Band (UWB) sensors to measure Channel Impulse Responses (CIRs) within a WSN. Determining an object position based on the CIR can be achieved by formulating an elliptical model. Based on this relation, we propose a CIR environmental mapping (CIR-EM) method, which represents a heatmap generation of the propagation environment based on the CIRs taken from radio communication signals. Along with providing imaging capabilities, this method also allows a more robust localization when compared to state-of-the-art methods. This paper provides a proof-of-concept of passive localization solely based on evaluating radio communication signals by conducting measurement campaigns in an anechoic chamber as a best-case environment. Furthermore, shortcomings due to physical layer limitations when using non-dedicated hardware and signals are investigated. Overall, this work lays a foundation for related research and further evaluation in more application-oriented scenarios.

Information-cartographic support for studying of potential transformations of geosystems for the digital atlas “Вaikal region: society and nature”

Based on the electronic atlas “Baikal Region: Society and Nature”, the problem of landscape-cartographic support for studying the transformations of vast territories is being solved. The transformation of geosystems is understood as changes in the natural environment due to spontaneous development or anthropogenic interference. In this context, potential and actual transformations are distinguished. The research of potential transformations is associated with a geographic forecast of possible changes in the state of the environment due to external impact, and current transformations include an assessment of its current ecological state. The area under investigation includes the territory of the Baikal basin, and the northern regions of Mongolia. This aspect is realized through the integration of a multitude of geographical data on the structure of natural systems, their sustainability and trends of anthropogenic transformations into a single target cartographic information system (CIS). The main requirement for the content of the target block of maps is the reliability of the results of a comprehensive research and their evidentiality if used to make management decisions to optimize the environment. A logical and methodological coherence of small-scale target mapping of the natural environment of a vast territory has been developed. The analysis of natural structures was carried out and a basic inventory map of geosystems of scale (M 1:5,000,000) was created. We developed a set of geosystem characteristics and carried out an environmental interpretation of information and integrated environmental mapping, aside from that implemented geoecological zoning of the territory. Based on the information synthesis of the obtained data and knowledge about the modern landscape structure of the region, the methods of polysystem analysis, we revealed the nature of sustainability, functions, value characteristics of geosystems and the patterns of their anthropogenic transformations. The presence of a single set of maps will provide a study of questions about potential and relevant transformations of geosystems. The small-scale maps of the Baikal region, compiled in a certain sequence on the basis of a single structural-hierarchical specialized classification, reflect a complex of environmental conditions that are important for making constructive-geographical, design, managerial, and environmental decisions.