Sen2Like: Paving the Way towards Harmonization and Fusion of Optical Data

Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this context, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions. For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes

Sensor modeling and validation for Linear Array aerial and satellite imagery

The Linear Array CCD technology is widely used in the new generation aerial photogrammetric sensors and also in the high-resolution satellite optical sensors. In comparison to the Matrix (frame/area) Array sensors, the Linear Array CCD sensors have smaller number of detectors to cover the same swath width. In addition, the flexibility is higher in the physical sensor design. The conventional film cameras used in aerial photogrammetry are manufactured in frame format. The first remote sensing sensors for Earth observation employed film cameras as well. The recent sensor technologies of the optical remote sensing satellites are replaced with the Linear Array CCDs. In case of the aerial photogrammetric sensors, medium and small format aerial cameras are produced only in the frame format. The development in large format cameras is twofold. The Linear Array CCD and Matrix Array CCD sensors have been present in the industry since the year 2000. Due to the geometric differences between the Linear Array cameras and the frame cameras, the conventional photogrammetric procedures for the geometric processing of the Linear Array CCD images should be redefined or newly developed. The trajectory modeling is one of the main concepts, which entered into the field of photogrammetry with the aerial and satellite pushbroom sensors. The modified collinearity equations are extended with mathematical functions to model the image trajectory in the bundle adjustment. This study encompasses the triangulation of Linear Array CCD images with the use of different trajectory models. The self-calibration models are partially adapted from the frame sensors in accordance with the physical structures of the Linear Array CCD sensors. In general, the triangulation and self-calibration of the aerial and the satellite Linear Array CCD images show similarities in terms of trajectory modeling and the physical definitions of the additional parameters. The main difference is in the number unknown parameters defined in the bundle adjustment, which is calculated as a function of the number of lenses, the trajectory model configuration, and the number of Linear Array CCDs used in the sensor. Therefore, similar sensor modeling and calibration approaches are applied in this study, with necessary adjustments for each system. In order to obtain high accuracy point positioning, high quality image trajectory measurement is crucial. The given trajectory can be modeled in the adjustment by using constant and linear correction parameters, as well as higher order polynomials. This study investigates the three different trajectory models with three different mathematical approaches. Two of the models are investigated at different levels of sophistication by altering the model parameters. Two different aerial Linear Array CCD sensors, the STARIMAGER of former Starlabo Corporation, Japan, and the ADS40 sensor of the Leica Geosystems, Heerbrugg, are used for the practical investigations. The PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) onboard of Japanese ALOS satellite launched by JAXA (Japan Aerospace Exploration Agency) in 2006 is the satellite Linear Array CCD sensor used for the application parts of this study. The two aerial Linear Array CCD sensors work with the TLS (Three-Line-Scanner) principle. Three or more Linear Array CCDs are located in the focal plane of a single lens with different viewing angles providing stereo capability. The PRISM sensor differs in the optical design with three camera heads, each associated with a different viewing angle. Due to the design differences between the sensors, two sets of additional parameters for self-calibration are applied in this study. The aerial TLS sensors share the same set of additional parameters due to similar interior geometries of the sensors. The self-calibration of the PRISM sensor uses a different set due to multiple lenses and also multiple CCD chips used to form each image line. The sensor orientation and calibration methods presented in this study are validated using a number of application datasets. The image datasets of the three sensors are acquired over specially established testfields. Triangulation results prove the importance of high quality trajectory measurements for accurate sensor orientation. When the given image trajectory has a low quality, a sophisticated trajectory model should be used together with a high number of ground control points. This study also shows that, despite their weaker sensor geometry, the Linear Array CCD sensors have reached the accuracy potential of the conventional frame imagery for point determination. In addition, similar to the conventional film sensors, self-calibration has proven as a powerful tool for modeling the systematic errors of the Linear Array CCD imagery, albeit the method should be applied with a great care

Geometric Accuracy Investigations of SEVIRI High Resolution Visible (HRV) Level 1.5 Imagery

GCOS (Global Climate Observing System) is a long-term program for monitoring the climate, detecting the changes, and assessing their impacts. Remote sensing techniques are being increasingly used for climate-related measurements. Imagery of the SEVIRI instrument on board of the European geostationary satellites Meteosat-8 and Meteosat-9 are often used for the estimation of essential climate variables. In a joint project between the Swiss GCOS Office and ETH Zurich, geometric accuracy and temporal stability of 1-km resolution HRV channel imagery of SEVIRI have been evaluated over Switzerland. A set of tools and algorithms has been developed for the investigations. Statistical analysis and blunder detection have been integrated in the process for robust evaluation. The relative accuracy is evaluated by tracking large numbers of feature points in consecutive HRV images taken at 15-minute intervals. For the absolute accuracy evaluation, lakes in Switzerland and surroundings are used as reference. 20 lakes digitized from Landsat orthophotos are transformed into HRV images and matched via 2D translation terms at sub-pixel level. The algorithms are tested using HRV images taken on 24 days in 2008 (2 days per month). The results show that 2D shifts that are up to 8 pixels are present both in relative and absolute terms.ISSN:2072-429

Development of a Smart City Concept in Virtual Reality Environment

There is an increasing interest in smart city concept as a technology-based alternative to conventional urban planning approaches. The design and implementation of smart cities require multidisciplinary efforts. As one of the first examples, a smart city concept was developed in Turkey in the year 2018 in collaboration with researchers and experts from various disciplines, such as geomatics and civil engineers, architects, computer scientists and urban planners. The developed concept aimed at designing a city district that targets sustainability, human-centricity, smartness, and safety, along with a sense of place and reflects local expectations in an unconstructed sub-urban area. In the project, the collaborative design process and the usability issues of the presentation environments have been of importance for geomatics professionals. Within the study, the existing settlement areas were modelled by using aerial photogrammetric data and combined with elements designed in various Computer-Aided design (CAD) software. In this paper, the smart city design elements, which originate from the design principles such as ensuring nature conservation, green, cultural, safe and smart living spaces, and social responsibility, are explained briefly. The city was presented to the stakeholders via Unity game engine for a realistic experience prior to construction. The potential of Virtual Reality (VR) environments for the design, modelling, and visualization of very high detailed smart city concept is presented and various issues are discussed. The model experiment videos with VR, web-based model and project video can be accessed via the web page www.bizimsehir.org.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

Snow Avalanche Susceptibility Mapping for Davos, Switzerland

Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors used for the avalanche susceptibility assessment include elevation, slope, plan curvature, profile curvature, aspect, topographic position index, topographic ruggedness index, topographic wetness index, land use and land cover, lithology, distance to road, and distance to the river. Two ML algorithms, the logistic regression (LR) and the random forest (RF), were comparatively assessed using validation data split from the training data (30/70). The prediction performances of both models were assessed based on the area under the receiver operating characteristic curve (ROC-AUC) value. Although the AUC value obtained from the LR method was relatively low (0.74), the value obtained from the RF (0.96) demonstrated high performance and usability of this approach. The results indicate that the RF method can successfully produce an avalanche susceptibility map for the region, although potential improvements may be possible by investigating various input features and ML algorithms as well as by classifying the starting and runout zones of the avalanche data separately. Furthermore, the accuracy is expected to increase by using a larger training dataset.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

A 3D Campus Application Based on City Models and Webgl

Scopu

Remote sensing for UN SDGs: A global analysis of research and collaborations

The Sustainable Development Goals (SDGs) provide a policy-making baseline for countries to overcome shortcomings and barriers for people and the planet Earth by 2030. Remote sensing (RS) enables evidence-based policy making and can contribute to realization of the SDGs by monitoring the indicators and evaluating the targets related to human and physical geography. This study exploited the RS research concerning the SDGs based on a Web of Science Core Collection database query [TS=((“remote sensing” OR “Earth observation*”) AND (“Sustainable Development Goal*”))] between 2016 and 2022 and by utilizing an artificial intelligence tool developed for SDG classification. We retrieved and analyzed articles (n = 308) using science mapping techniques. Remote Sensing is the most relevant journal publishing articles related to this theme. While the dominance of Chinese institutes in terms of authors' affiliation is clear, the highest collaboration network is between the USA and China. Our findings revealed that subjects related to carbon storage, ecological quality and impervious surface draw attention of researchers increasingly and becoming trend topics. From the SDG classification results, SDG 15 and SDG 11 emerged as the most prevalent subjects related to the RS research. Given the exponential increase in the number of studies, we recommend to employ bibliometric analysis and science mapping tools to systematically identify research patterns and gaps in both fields, as manual efforts may progressively become challenging

Improvement of Disability Rights via Geographic Information Science

Rights, legal regulations, and practices often arise from societal and scientific developments, and societal transformations may originate from new legal regulations as well. Basic rights can be re-defined with advancements in science and technology. In such an evolutional loop, where mutual supply is obvious, combined legal and technological frameworks should be exercised and developed for practicing human rights. The main aim of this article is to propose a conceptual and methodological framework for the improvement of disability rights in the light of recent advancements in geographic information science (GIScience), in particular for those with motor disabilities, for whom questions related to “where” are essential. The concept of disability is discussed, considering different aspects, and a new methodological framework is proposed in which Geographic Information Systems (GIS), volunteered geographic information (VGI) and citizen science are at the core. In order to implement the framework at the national and international levels, a spatial data model should be developed first. The new data collection and interpretation approaches based on VGI, citizen science, and machine learning methods may help to realize equal rights for people with motor disabilities, by enabling improved access to education, health, and travel

Reconstruction and Efficient Visualization of Heterogeneous 3D City Models

The increasing efforts in developing smart city concepts are often coupled with three-dimensional (3D) modeling of envisioned designs. Such conceptual designs and planning are multi-disciplinary in their nature. Realistic implementations must include existing urban structures for proper planning. The development of a participatory planning and presentation platform has several challenges from scene reconstruction to high-performance visualization, while keeping the fidelity of the designs. This study proposes a framework for the integrated representation of existing urban structures in CityGML LoD2 combined with a future city model in LoD3. The study area is located in Sahinbey Municipality, Gaziantep, Turkey. Existing city parts and the terrain were reconstructed using high-resolution aerial images, and the future city was designed in a CAD (computer-aided design) environment with a high level of detail. The models were integrated through a high-resolution digital terrain model. Various 3D modeling approaches together with model textures and semantic data were implemented and compared. A number of performance tuning methods for efficient representation and visualization were also investigated. The study shows that, although the object diversity and the level of detail in the city models increase, automatic reconstruction, dynamic updating, and high-performance web-based visualization of the models remain challenging