bluuebunny/crossref_metadata_2025 huggingface dataset:
FastIsostasy v1.0 – An accelerated regional GIA model accounting for the lateral variability of the solid Earth
Authors: Swierczek-Jereczek Jan , Montoya Marisa , Latychev Konstantin , Robinson Alexander , Alvarez-Solas Jorge , Mitrovica Jerry
Published: December 2023

Abstract. The vast majority of ice-sheet modelling studies rely on simplified representations of the Glacial Isostatic Adjustment (GIA), which, among other limitations, do not account for lateral variations of the lithospheric thickness and upper-mantle viscosity. In studies using 3D GIA models, this has however been shown to have major impacts on the dynamics of marine-based sectors of Antarctica, which are likely to be the greatest contributors to sea-level rise in the coming centuries. This gap in comprehensiveness is explained by the fact that 3D GIA models are computationally expensive, seldomly open-source and require the implementation of an iterative coupling scheme to converge with the history of the ice-sheet model. To close this gap between "best" and "tractable" GIA models, we here propose FastIsostasy, a regional GIA model capturing lateral variations of the lithospheric thickness and mantle viscosity. By means of Fast-Fourier transforms and a hybrid collocation scheme to solve its underlying partial differential equation, FastIsostasy can simulate 100,000 years of high-resolution bedrock displacement in only minutes of single-CPU computation, including the changes in sea-surface height due to mass redistribution. Despite its 2D grid, FastIsostasy parametrises the depth-dependent viscosity in a physically meaningful way and therefore represents the depth dimension to a certain extent. FastIsostasy is here benchmarked against analytical, 1D and 3D GIA solutions and shows very good agreement with them. It is fully open-source, documented with many examples and provides a straight-forward interface for coupling to an ice-sheet model. The model is benchmarked here based on its implementation in Julia, while a Fortran version is also provided to allow for compatibility with most existing ice-sheet models. The Julia version provides additional features, including a vast library of time-stepping methods and GPU support.
SYSTEMS AND ARCHITECTURAL SUPPORT FOR OPEN DATA PRINCIPLES: A MARINE EARTH OBSERVATION PERSPECTIVE
Authors: Sibolla B. , Molapo R. , Vhengani L. , Mdakane L.
Published: December 2023

Abstract. The GMES and Africa, MarCOSIO project, promotes the sustainable management of marine resources, improves marine governance, and stimulates the growth of the blue economy in the Southern African and Indian ocean region. Because of its mandate, a wide range of applications, and regional view that incorporates eight countries, the MarCOSIO Information Management System adopts and continues to strive towards implementation of open data principles. This paper presents considerations of the MarCOSIO information management systems towards the adoption and implementation of open data, FAIR and TRUST principles, to ensure the sustainability of its services and offerings. The implementation of the system is evaluated against criteria that assess the alignment to FAIR, TRUST, and Open Data principles. An assessment of the results shows that the system aligns closely, though not fully, with the FAIR and TRUST principles. This is mainly due to the use of reputable open-source tools that implement well-known and accepted standards. On the basis of the services provided, the system cannot be fully open, as in some cases the need for security outweighs the openness. This is specifically true for cases where there is a need to protect personal information, and public disclosures are prohibited by law. The use of open data from the European Commission has allowed the rapid development of regionally relevant solutions that, in turn, have remarkably improved decision making in promoting the protection and growth of the ocean economy. It is one of the goals of the open data directive to increase public participation in the achievement of economic growth.
3D GEO-REFERNECING OF FREE MODEL THROUGH 3D-TO-2D ALIGNMENTS AT THE CE-5 LANDING SITE
Authors: Li Y.
Published: December 2023

Abstract. The descent camera mounted on the CE-5 lander captured abundant images during the landing, which can be used to generate local terrain models with details from sub-meter to millimetre scales. But unlike Earth, high-precision control data are unavailable on the Moon especially for small regions like the landing sites, making it hard to define exact geo-reference of the local terrain models, which is essential for topographic and geomorphological mapping and analyses. To tackle this problem, this paper proposes a method for geo-referencing free 3D models generated from the CE-5 descent images based on low-resolution geo-referenced orbiter data. On account to the large discrepancy between the accuracies in the horizontal and vertical directions of orbiter data, the proposed method used two independent alignments in 3D and 2D spaces respectively to reduce the impacts of accuracy unevenness of the reference data. The 3D alignment is performed based on mass features to align the base-plane of the free model, and the 2D alignment is performed based on matched point features projected on the base-plane to define the scale and orientation of the model. After these the alignments, the free model is ultimately enriched with geo-reference information and used to generate fine-scale DEM and DOM of the CE-5 landing site. The average horizontal distance between the referenced data and the geo-referenced data is 2.19 m with a small standard deviation of 1.48 m, and the average height difference between the generated DEM and the reference DEM is 2.78 m, indicating a reliable 3D geo-referencing result.
VIEW GRAPH CONSTRUCTION FOR LARGE-SCALE UAV IMAGES: AN EVALUATION OF STATE-OF-THE-ART METHODS
Authors: Liu J. , Ma Y. , Jiang S. , Li Q. , Jiang W. , Wang L.
Published: December 2023

Abstract. Structure from Motion (SfM) is a 3D reconstruction framework that has achieved great success on large-scale Unmanned Aerial Vehicle (UAV) images. Due to the high time consumption of feature matching, a matching candidate subset is obtained by image retrieval to improve efficiency. Bag of Word (BoW) based image retrieval has been widely used in SfM systems, but the large number of local features and the high dimension of the BoW vector cause the retrieval method time-consuming. Vector of Locally Aggregated Descriptors (VLAD) and learning-based NetVLAD perform well in image retrieval, and these vector representation methods are evaluated in this study. After images are transformed into vectors, Nearest Neighbour (NN) searching methods like Brute-force and KD-Tree are used to find similar images. But as the number of images and the vector dimension increase, Approximate Nearest Neighbour (ANN) searching methods like Hierarchical Navigable Small World (HNSW) and Locality-Sensitive Hashing (LSH) are considered to replace NN searching to avoid efficiency degradation. These vector searching methods are also evaluated in this study. The test results demonstrate that the optimal method VLAD with HNSW can speed up about 100 times in finding matching candidate subset. A view graph that guides scene partition and sub-scene reconstruction in parallel SfM can be created by the optimal method. With this view graph construction method, the efficiency of SfM is significantly improved.
ASSESSMENT OF STEC ESTIMATION QUALITY USING GNSS PPP FIXED
Authors: Monico J. F. G. , de Oliveira Jr. P. S. , Pereira V. A. S. , Machado S. B. L.
Published: December 2023

Abstract. GNSS (Global Navigation Satellite System) data can be used for geodetic remote sensing, particularly for monitoring the ionosphere in the context of Space Weather. One of the important parameters derived from GNSS measurements for ionospheric analysis is the Slant Total Electron Content (STEC). By utilizing GNSS data from multiple frequencies or even a single frequency, the STEC can be computed using an appropriated linear combination, like geometry free. However, when computing an ionospheric gradient between two IPP (Ionospheric Pierce Point) from the same satellite, the precision of the STEC estimate can become a limiting factor. In some cases, the uncertainty in the estimate may be greater than the actual gradient value itself. This poses challenges, especially for augmentation systems like GBAS (Ground Based Augmentation System), where accurate ionospheric gradients are crucial. An alternative approach to improve these limitations is to estimate the STEC using a different approach, like Precise Point Positioning (PPP). For such case, the coordinates of the GNSS stations are constrained to known values (PPP-Fixed), while other parameters such as clock biases, tropospheric delays, and ionospheric delays (including STEC) can be estimated. The results of an experiment carried out to assess the quality of STEC for such application are presented and have shown good results. Ionospheric gradients are agreed in the mm level.
DYNAMICAL VARIATIONAL AUTOENCODERS AND KALMANNET: NEW APPROACHES TO ROBUST HIGH-PRECISION NAVIGATION
Authors: Shen D. , Ma Y. , Liu G. , Hu J. , Weng Q. , Zhu X.
Published: December 2023

Abstract. Kalman filters, recognized as a traditional and effective inference algorithm based on state space models (SSM), have been extensively applied in the fields of navigation and mapping. However, their performance will degrade when facing model assumption mismatches, such as non-linear dynamics and non-Gaussian correlated noises. The model-based deep learning methods overcome these mismatches by combining the domain knowledge of the model-based methods and the expressiveness of the data-driven deep learning methods, and thus can provide a promising solution for addressing high-dimensional and nonlinear challenges. This paper presents a succinct overview of the principles, inference model, and training methodology employed in model-based deep learning methods, with particular focus on the KalmanNet and the Dynamical Variational Autoencoder (DVAE). Furthermore, it implements KalmanNet on robust and high-precision navigation and positioning problem. The experimental results substantiate the feasibility of achieving navigation and positioning accuracy comparable to that of the Extended Kalman Filter (EKF), while simultaneously exhibiting enhanced robustness, albeit at the cost of some computational overhead.
A NOVEL GPS FAULT DETECTION AND EXCLUSION ALGORITHM AIDED BY IMU AND VO DATA FOR VEHICLE INTEGRATED NAVIGATION IN URBAN ENVIRONMENTS
Authors: Wang Y. , Sun R.
Published: December 2023

Abstract. High accuracy positioning is crucial for various applications that require accurate and reliable positioning data, such as autonomous vehicles (AVs), agriculture, and intelligent transportation. Global Positioning System (GPS) is widely used in integration with Inertial Measurement Unit (IMU) and Visual Odometry (VO) to implement an accurate and robust navigation system. However, the performance of the integrated system can be severely degraded in urban environments due to non-line-of-sight (NLOS) reception and multipath interference. To overcome these challenges, a novel Fault Detection and Exclusion (FDE) algorithm aided with IMU and VO measurements is proposed to identify and isolate the contaminated satellite signals. The algorithm utilizes instantaneous measurements from IMU and VO to predict the current vehicle position, enabling the estimation of pseudorange errors in GPS measurements. A FDE algorithm based on Hierarchical clustering is then developed to identify GPS signals with significant errors based on the predicted pseudorange error. An experimental field test was conducted using a land vehicle to evaluate the effectiveness of the proposed algorithm. The results show that the GPS/IMU/VO integrated navigation system with the proposed FDE algorithm has significantly improved the positioning accuracy and reliability compared to the traditional system. The proposed algorithm achieves a positioning accuracy with a 3D Root Mean Square Error (RMSE) of 11.18m in urban environment, making an improvement of 68.2% over the traditional GPS/IMU/VO integrated navigation system.
A SUPERPOINT NEURAL NETWORK IMPLEMENTATION FOR ACCURATE FEATURE EXTRACTION IN UNSTRUCTURED ENVIRONMENTS
Authors: Petrakis G.
Published: December 2023

Abstract. Feature extraction plays a crucial role in visual localization, SLAM (Simultaneous Localization and Mapping) and autonomous navigation, by enabling the extraction and tracking of distinctive visual features for both mapping and localization tasks. However, most of the studies, investigate the efficiency and performance of the algorithms in urban, vegetated or indoor environments and not in unstructured environments which suffers by poor information in visual cues where a feature extraction algorithm or architecture could base on. In this study, an investigation of SuperPoint architecture’s efficiency in keypoint detection and description applied to unstructured and planetary scenes was conducted, producing three different models: (a) an original SuperPoint model trained from scratch, (b) an original fine-tuned SuperPoint model, (c) an optimized SuperPoint model, trained from scratch with the same parametarization as the corresponding original model. For the training process, a dataset of 48 000 images was utilized representing unstructured scenes from Earth, Moon and Mars while a benchmark dataset was used aiming to evaluate the model in illumination and viewpoint changes. The experimentation proved that the optimized SuperPoint model provides superior performance using repeatability and homography estimation metrics, compared with the original SuperPoint models, and handcrafted keypoint detectors and descriptors.
4D RADAR/IMU/GNSS INTEGRATED POSITIONING AND MAPPING FOR LARGE-SCALE ENVIRONMENTS
Authors: Wang B. , Zhuang Y. , El-Bendary N.
Published: December 2023

Abstract. 4D millimeter-wave radar can work in harsh weather conditions such as fog and snow, and measure the position and radial Doppler velocity of objects in three-dimensional space. Some existing methods perform positioning by fusing 3D velocity or point cloud matching information estimated by 4D radar with IMU information. However, the sparsity of radar point clouds and the interference of large amount of noise lead to low accuracy of odometry, and most of the existing work cannot achieve the global consistent mapping with radar point clouds. In this paper, a 4D radar/IMU/GNSS localization and mapping method, G-iRIOM, is proposed. We tightly coupled IMU measurement, Doppler observation information and point cloud matching information from 4D radar by iterative extended Kalman filtering (iEKF) method, and introduced GNSS RTK and loop closure information as global observation to correct the positioning drift of the odometry. The experimental results show that the tightly coupled Doppler velocity information can effectively improve the control of the pose on the local point cloud position, thus enhancing the mapping accuracy. Meanwhile, the introduction of GNSS and loop closure information can significantly improve the positioning accuracy of 4D radar odometry as a kind of global observation, and achieve global consistent mapping of large-scale outdoor scenes based on 4D radar point clouds.
DIFFERENTIAL SAR TOMOGRAPHY OF LARGE-SCALE WATER CONSERVANCY PROJECTS UNDER STEEP TERRAIN – THE CASE STUDY OF LAXIWA HYDROPOWER STATION
Authors: Li L. , Pang L. , Li C. H.
Published: December 2023

Abstract. Differential SAR Tomography (D-TomoSAR), as an extension of InSAR technology, combines D-InSAR and TomoSAR technology to achieve imaging in the height-deformation rate (s-v) plane. It not only solves the layover problem of SAR imaging, but also obtains the height and deformation rate of each scatterer within the image element. The technique is currently mainly applied to complex scenes in urban areas where the layover problem is serious, and the layover effect also exists for hydropower plants with extremely steep slopes. In this paper, the Differential SAR Tomography technique is applied to the four-dimensional imaging of hydraulic engineering for the first time, taking the La Siwa hydropower station as an example. This experiment establishes the signal model based on permanent scatterer points, so interferometric processing and PS point selection should be performed to obtain the differential interferogram sequence and PS points in the region; the orthogonal matching pursuit (OMP) algorithm is selected for differential SAR tomography imaging processing to reconstruct the elevation-deformation rate backward scattering profile. The experiments use the 23-view TerraSAR-X satellite one-meter resolution time series image dataset for deformation monitoring of the Laxiwa hydropower dam in GuiDe County, Qinghai Province, China, and finally the resolution and reconstruction estimation performance are evaluated by theoretical analysis and application case study. A comparison with the traditional InSAR technique shows that the differential SAR tomography technique not only maintains the advantages of high resolution, but also significantly improves the probability of accurate reconstruction of scattered points, and achieves higher accuracy in estimating the deformation of hydropower dams. This paper mainly discusses the application of differential SAR tomography technology in water conservancy projects, hoping to provide reference and help for the future large-scale application of differential SAR tomography technology in hydroelectric dam deformation monitoring.
EXPLORING URBAN FUNCTIONAL ZONES BASED ON MULTI-SOURCE SEMANTIC KNOWLEDGE AND CROSS MODAL NETWORK
Authors: Chen J. , Peng S. , Zhang H. , Lin S. , Zhao W.
Published: December 2023

Abstract. The refined identification of urban functional zones can provide important basic data and decision-making basis for the formulation of urban spatial development planning, effective relaxation of urban spatial development planning, effective relaxation of urban functions, and optimal allocation of resource space. The multi-source spatiotemporal data represented by multi-source geographic data, social perception data, and thematic data have been widely used in various fields, providing new data sources for the refined identification of urban functional areas. However, there are significant differences in the data generation sources, collection methods, and storage organization formats of multi-source data. In this paper, we propose a method for exploring urban functional zones based on Multi-source Semantic knowledge and deep Coupling Model (MSCM). Our approach integrates information from multiple sources and incorporates the semantics of urban functional zones into a knowledge graph, enabling effective fusion and mining of multi-source data.This method can improve the credibility and precision of the results, providing a richer research perspective for refined urban functional zoning. The results of this paper have important theoretical value and practical significance for the construction of identification, labelling, and monitoring tools for engineering smart cities.
RESEARCH ON THERMAL ENVIRONMENT ANALYSIS OF PHOTOVOLTAIC POWER PLANTS BASED ON REMOTE SENSING IMAGES
Authors: Cheng H. , Huang H. , Yang J. , Pang R. , Bian J.
Published: December 2023

Abstract. As a representative of new energy, photovoltaic power stations have developed rapidly in China, with annual installed capacity increasing. At present, research on photovoltaic power plants mainly concentrates on carbon neutrality based on the whole life cycle, and there are few studies on the thermal environment problems that may be caused by photovoltaic power plants. This paper takes the area where the photovoltaic power station is located in Quyang County, Baoding City, Hebei Province, and the surrounding mountains as the research area. It uses Gaofen-1 and Landsat 8 remote sensing images to study the changes in land cover and surface temperature before and after the construction of mountain photovoltaic power stations over a period of 8 years from 2013 to 2020.Given the lack of quantitative research on the impact of mountain photovoltaic power stations on the surrounding environment using remote sensing technology, this paper focuses on the influence of the distribution and proportion of mountain photovoltaic power stations on the spatial pattern of the thermal environment in the region. Through experimental verification and result analysis, it can be seen that under the geographical location and time conditions of the study area, the proportion of photovoltaic power stations shows a strong correlation with the average surface temperature, which is one of the main factors causing the temperature rise in mountainous areas. It is a land cover type with heat island effects in the traditional sense.
LAND SUBSIDENCE IN WUHAN REVEALED USING A MULTI-SENSOR INSAR TIME SERIES FUSION APPROACH
Authors: Jiang H. , Balz T. , Li J.
Published: December 2023

Abstract. Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely utilized for topographic, geological, and natural resource investigations. However, many existing InSAR studies on ground deformation are limited to relatively short observation periods and single sensors. This paper introduces a novel method for fusing multi-sensor InSAR time series data, specifically designed to address scenarios involving partial overlaps and temporal gaps. The method employs a new Power Exponential Knothe Model (PEKM) to fit and fuse overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses data during temporal gaps in the series. In this study, the city of Wuhan in China was selected as the experimental area. SAR datasets from COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019), and Sentinel-1 (2019–2021) were fused to map long-term surface deformation over the past decade. An independent InSAR time series analysis from 2011 to 2020, based on 230 COSMO-SkyMed scenes, was used as a reference for comparison. The correlation coefficient between the fusion algorithm’s results and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The overall correlation coefficient of 0.78 demonstrates that the proposed algorithm achieves a similar trend as the reference deformation curve. Based on the long time series settlement results obtained through fusion, a detailed analysis of the causes of settlement was conducted for several subsidence zones. The subsidence in the Houhu area is primarily attributed to the consolidation and compression of soft soil. Soil mechanics were employed to estimate the expected completion time of subsidence and calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression, gradually stabilizing over time. Furthermore, the subsidence curve observed in the area around Xinrong reveals that the construction of an underground section of subway Line 21 has caused significant settlement in that particular region. The high temporal granularity of the PSInSAR time series also enables precise detection of a rebound phase following a major flooding event in 2016. The experimental results demonstrate the accuracy of the proposed fusion method in providing robust time series for analyzing long-term land subsidence mechanisms. Additionally, these findings unveil previously unknown characteristics of land subsidence in Wuhan, thus clarifying the relationship with urban causative factors.
UNSUPERVISED WINTER WHEAT MAPPING BASED ON MULTI-SPECTRAL AND SYNTHETIC APERTURE RADAR OBSERVATIONS
Authors: Li H. Y. , Lawrence J. A. , Mason P. J. , Ghail R. C.
Published: December 2023

Abstract. Annual meteorological variations and the impact of climate change in recent years impacted on agricultural production and distribution. Since wheat is a main food resource and the most widely grown crop in the world, it is essential to ensure the sustainability of its production. Therefore, accurate wheat mapping is essential for agricultural production forecasts. Multi-spectral satellite image analysis, including supervised machine learning (ML) methods, has been applied to wheat and other crop mapping, but such passive, optical imaging approaches are strongly influenced by weather conditions and cloud cover, whilst the supervised ML algorithms are highly reliant on manual labelling and ground control data. To avoid the limitation of weather and cloud, this research integrates Sentinel-1 Synthetic Aperture Radar (SAR) data with Sentinel-2 multi-spectral image products to achieve more reliable and accurate winter wheat mapping. Normalised Difference Vegetation Index (NDVI) retrieved from multi-spectral imagery and Sentinel-1’s dual-polarisation radar signals, VV, VH, acquired in different time series, are used as key inputs to an unsupervised ML model based on Dynamic Time Warping (DTW) and hierarchical clustering to prevent time-consuming manual labelling. The chosen study area lies in Norfolk, UK. The result of winter wheat classification with NDVI time series data in this study reaches 72% accuracy, but the improved classification integrating NDVI, VH, VV and VH/VV values achieves 86% accuracy. Future research will focus on optimizing the ML model with multi data integration in addition additional research sites will test more complicated scenarios and multiple crop classification.
AGROECOLOGICAL PRINCIPLES AND ITS GAPS IN ADAPTION IN TERAI FARMING SYSTEM, NEPAL
Authors: Mandal U. K. , Khanal N. R. , Nepal P. , Kumari K.
Published: December 2023

Abstract. The present paper examines agroecological principles identified by FAO and its gaps by local farmers in terai farming system of Nepal. Household questionnaire survey, focus group discussion and KII were major tools of required information collection with regard to gaps of agroecology principles in their farming system. Agro-ecological principles have been examined with current farming practices carried out by local farmers. The gap analysis has been done with respect to ten agroecological principles: Efficient use of resources, balance use of chemical and organic fertilizer, , crop diversity, co-creation of knowledge, recycling of farm waste, synergy of crop, livestock and forestry ,human and social value, circular economy, cultural and food tradition, land and natural resource governance. 87.8 percent of households were used flooding method of surface irrigation against to principle of efficiency and deficit of water leading to wastage of water and constraining self-generating nutrient capacity of nature. 91.60 % of total cropped area was dominated by paddy - wheat based cropping system against to the principle of crop diversity. 72.4 % farmers used farm yard manure in limited temporal and spatial coverage area ranging from 0.03 ha to 1.69 hectare with the average of 0.39 ha and three months of year against the principle of recycling farm wastage to farm. Almost 14 % of households were having agroforestry practices against to the synergies of crop, livestock and forestry. Participation of women and youth in farming practices was found decreased due to remittance-led family economy. Geospatial technology-RS, GIS and GPS were extensively applied in preparation of all these agroecological resource maps required for agroecological studies and creation of their geo-database for sustainable land use planning in Nepal. Finally, the study necessitated the government policy to be instutionalized towards agroecological concept and hoped the methodology developed for this study could guide for achieving this goal.
EXPLORING THE POTENTIAL OF OPEN-DATA FOR OCEANS MONITORING WITH AI ANALYTICS
Authors: Mdakane L. W. , Sibolla B. , Haupt S.
Published: December 2023

Abstract. Monitoring vessel activity in the ocean plays an essential role in ensuring maritime safety, environmental monitoring, and fisheries management. This article explores the importance of vessel tracking and investigates the potential of using open data for ocean monitoring using AI analysis. As satellite data demand increases, new technologies are being developed to address challenges and improve accessibility for a wider user base. This article discusses the use of AI analytics to process multiple datasets, identify ships, and track their movements. In addition, it emphasises the importance of cloud geoprocessing for accessing and analysing vast spatial data, resulting in improved decision-making and operational efficiency. In general, this article provides information on how vessel tracking can be improved for maritime safety, security, and environmental protection using open remote sensing and AI-based analysis datasets.
PHILIPPINE EARTH OBSERVATION SATELLITE MISSIONS AND APPLICATIONS: A DECADAL SURVEY
Authors: Perez G. J. , Pangasinan J. , Refran J. C. , De Maligaya J. , Enricuso O. , Marciano Jr. J. J.
Published: December 2023

Abstract. The Decadal Survey was conducted to generate recommendations for integrated and sustainable conduct of space-based environmental monitoring and Earth observation (EO) science programs. Inputs from members of the scientific community and various stakeholders were synthesized to gain an in-depth understanding of the current status and gaps in our space- and non-space-based observation systems in addressing challenges that may persist or arise in this decade. The Decadal Survey is conducted in two phases. The first phase is the Request for Information 1 (RFI-1), where 29 experts from the scientific community were asked to describe the most pressing challenges in their field and the corresponding objectives to address these challenges. A total of 54 challenges and 94 objectives were identified across six focus areas. The second phase consisted of the Delphi Survey where stakeholders from the government, academe, private sector, and non-government organization were asked to rank the challenges and objectives identified from Phase 1. A Request for Information 2 (RFI-2) is conducted to gather information on current programs and projects and institutional capabilities in relation to space science & technology applications (SSTA). Six focus areas (FAs) were identified to adequately cover the components of Earth observation. The assessment and validation of the prioritization of the challenges and objectives resulted in the identification of the top challenges per focus area. These priority challenges and objectives were then compared with the current SSTA technology capabilities of the agencies and institutions to identify common initiatives and gaps or areas for improvement. The results provided a glimpse of how the country’s current SSTA capabilities and initiatives are implemented to solve different scientific challenges and objectives. Looking at the list of previous and ongoing projects, much focus has been made on addressing challenges in resource management and disaster risk reduction and management. Fewer programs and projects center on climate and Earth processes studies. Although most of the identified challenges were matched with at least one ongoing project, there is also concern about the continuity and sustainability of these projects. The Decadal Survey provided information regarding the priority objectives, the target observables, and the mission specifications and timelines from the program of record. The collated information was used to conduct a mission gap analysis that eventually determined the satellite payloads needed to address most of the priority challenges and objectives. The performed gap analysis led to the identification of three instrument gaps that will serve as the basis of future EO satellite missions.
GIS INTEGRATION OF LAND COVER WITH NIGHT-TIME LIGHTS FOR SPATIOTEMPORAL EVALUATION OF URBAN EXPANSION
Authors: Singh S. , Jain K. , Shukla A.
Published: December 2023

Abstract. Monitoring the urbanisation process requires accurate knowledge of the present and historical spatial extents of built-up area. Understanding the quantification of urban growth is a crucial component of urban and environmental planning. The current research is focussed on evaluating the spatiotemporal change evaluation of built-up area by using a combination Landsat 8 and NPP-VIIRS datasets with GIS integration. Firstly, spatial change analysis of the built-up area was done by employing the Land Use and Land Cover (LULC) classification methodology for the years 2014 and 2022. This was done using the Random Forest (RF) classifier within the Google Earth Engine (GEE) platform. Secondly, the change in built-up area was evaluated with respect to the NTL data. The night-time light (NTL) images detect artificial lights being radiated from cities at night. The NTL data was categorised into three classes and later used to categorise the built-up area into ‘core urban’, ‘peri-urban’ and ‘rural’. The proportion of 'core urban' pixels experienced substantial growth, increasing from 9.8% to 15.5% of the total pixels between 2014 and 2022. This represents a notable increase of 58.75%. Conversely, the 'peri-urban' pixels saw a smaller increase of 4.6%, while the 'rural' pixels witnessed a decline of 8.7%. The average built-up density of the 'core urban' area exhibited an increase from 0.385 in 2014 to 0.398 in 2022. These changes can be attributed to the progressive conversion of 'rural' pixels into 'peri-urban' areas, and subsequently, the transformation of 'peri-urban' areas into 'core urban' regions.
QUALITY VERIFICATION OF OVERALL PLANNING SATELLITE REMOTE SENSING IMAGES PRODUCT FOR REAL 3D CHINA CONSTRUCTION PROJECT
Authors: Yin S. , Chen H. , Li H. , Ma C. , Tu H.
Published: December 2023

Abstract. The overall planning of satellite remote sensing image products is an important part of the real 3D China construction project and an important measure for the overall management of remote sensing images. During the two years of implementation of the project in 2021 and 2022, we have organized and completed 9 versions of 2-meter resolution satellite remote sensing image products, each of which covers the land territory of the whole country (not less than 95% in principle). Image products are widely used in natural resources investigation and monitoring, basic surveying and mapping, geological survey, natural resources supervision, law enforcement and other fields, which effectively improves the guarantee capability of surveying and mapping geographic information services. Taking the 2022 2-meter resolution integrated satellite remote sensing image product as the research object, this paper first introduces the highlights of the product itself, that is, timeliness, availability and reliability. then the product quality is studied from six aspects: spatial reference system, position accuracy, logical consistency, time accuracy, image quality and attachment quality. On this basis, according to the characteristics of different geographical regions, seasonal time series, topography and geomorphology, 100 representative verification samples were selected from the overall satellite remote sensing image products in 2022 for experiments, the distribution law of product quality characteristics was analyzed, and the main quality problems were summarized.
TRANSFORMER-BASED METHOD FOR SEMANTIC SEGMENTATION AND RECONSTRUCTION OF THE MARTIAN SURFACE
Authors: Li Z. , Wu B. , Chen Z. , Ma Y.
Published: December 2023

Abstract. The last decade has witnessed a great advance in deep space exploration, such as the rover missions to Mars. Semantic information on the Martian surface is garnering more attention, for its ability to distinguish the surface landforms for rover traverse planning and facilitating 3D reconstruction. The state-of-the-art studies on semantic segmentation exclusively leveraged transformer-based methods, and the results have been verified to outperform the traditional convolutional neural networks. However, few datasets concerning the Martian surface have been generated, and the publicly available network models were all trained on the common Earth dataset. Constructing a pixel-wise semantic segmentation dataset requires lots of human labor, especially for training a large transformer network. Furthermore, the results of semantic segmentation were typically used for intuitive visualization but seldom exploited in the 3D reconstruction pipeline. To address these problems, this paper presents the following three contributions: (1) introducing an approach to generate a large dataset for Mars in a semi-automatic way; (2) development of a novel variant of transformer designed for multi-view semantic segmentation to improve the accuracy; (3) development of a semantic-aware dense image matching method for improved matching performances assisted with the semantic information. Experimental results using the dataset collected at the Zhurong landing site on Mars have shown superior performances of the proposed methods as compared with traditional methods.
RELATIVE GEOMETRIC CORRECTION OF MULTIPLE SATELLITE IMAGES BY RIGOROUS BLOCK ADJUSTMENT
Authors: Ban S. , Kim T.
Published: December 2023

Abstract. With the development of satellite imaging technology, many Earth observation satellites have been launched and almost all areas of the Earth are daily covered by satellite images. It is promising to utilize such images in change detection, monitoring, semantic classification, etc. To obtain geometric information on each pixel in images, Rational Polynomial Coefficients (RPCs) of a Rational Functional Model (RFM) are provided with satellite images. However initial RPCs may include geometric errors. Therefore, errors in initial RPCs must be corrected before utilization. In this study, we attempt to perform rigorous block adjustments for refining RFMs of overlapping satellite images without ground control points. Our rigorous block adjustment method operated based on automatically extracted tie-points within overlapping areas. It estimates the optimal adjustment coefficients of RPCs and ground coordinates of tie-points. We achieved relative geometric correction of multiple satellite images by transforming the images based on the estimated adjustment coefficients. As a result, an initial RFM model with an error of around 21.73 pixels was corrected to within 1 pixel, and the reprojection error of check points decreased to 0.87 pixels. We also confirmed that our method showed more accurate results than general image registration methods, such as 2D homography transformation.
ESTIMATION OF SENSOR OFFSETS FOR A UAV PLATFORM USING TIEPOINTS ONLY
Authors: Kim C. , Lim P.-C. , Kim T.
Published: December 2023

Abstract. In a UAV system, physical sensor offsets occur between an observation sensor and a GPS/IMU sensor which represents the position of UAV. The difference of angle between the GPS/IMU sensor’s axis and the observation sensor’s axis is referred to as boresight angle. The difference in physical position between the two is referred to as lever-arm. It is important to obtain accurate offset values in order to utilize UAVs in rapid mapping manner. Due to the sensor offsets, misalignment error can be caused when generating a mosaic image. Offset values can be measured by using extensive ground control points. However, this is very costly and time-consuming. In this study, we describe an estimation method for sensor offsets using tie-points and re-weighted least square estimation method. The proposed method consists of 5 steps. Firstly, a frame images for the target area were classified into image strips based on the kappa value of initial EOPs (Exterior Orientation Parameters), after which tie points were extracted between adjacent images. Secondly, strip bundle adjustment was performed to update initial EOPs using images with the same flight direction. Thirdly, tie-points between adjacent strips were extracted. Fourthly, block bundle adjustment was performed in all images, using all extracted tie-points. Finally, a mosaic image is generated using the EOPs value to which the estimated sensor offset is applied. From this study, we confirmed that the sensor offset of the platform could be estimated only with the tie-points extracted between adjacent images. And we confirmed that misalignment was adjusted when generating mosaic image. We expect that our research makes UAV system to be operated more fluently.
AUTOMATED FINE-SCALE FOREST INVENTORY USING BACKPACK LIDAR – A STRATEGY BASED ON FEATURE EXTRACTION, MATCHING, AND TRACKING FROM INTEGRATED SCANS
Authors: Rastiveis H. , Zhou T. , Zhao C. , Fei S. , Habib A.
Published: December 2023

Abstract. Backpack LiDAR systems are gaining popularity due to their rapid data acquisition, portability, and cost-effectiveness. However, using backpack LiDAR in forest poses challenges, such as GNSS signal attenuation under the canopy, leading to inaccurate trajectory estimates and misregistered point clouds. This paper aims at presenting a novel method that addresses this challenge by leveraging the integrated scans (IS) concept to enhance point cloud quality for automated forest inventory. The proposed method for forest inventory using ISs consists of four key steps: (i) Integrated scans (IS) generation, (ii) feature extraction, matching, and tracking, (iii) trajectory enhancement, and (iv) tree biometric extraction. Firstly, IS point clouds are generated based on the initial GNSS/INS trajectory. Secondly, reliable forest features such as tree trunks and ground patches are extracted, matched, and tracked across ISs. These features are then utilized in the trajectory enhancement step, where a non-linear Least Squares Adjustment (LSA) technique is used to minimize discrepancies among the features to enhance the trajectory. The resulting point clouds, based on the improved trajectory, are used to extract tree biometric information. The proposed method was evaluated using two distinct datasets collected with different systems. The evaluation results, both qualitatively and quantitatively, validate the effectiveness of the proposed method, showcasing its potential for fine-scale forest inventory applications.
WOOD-LEAF UNSUPERVISED CLASSIFICATION OF SILVER BIRCH TREES FOR BIOMASS ASSESSMENT USING OBLIQUE POINT CLOUDS
Authors: Spadavecchia C. , Campos M. B. , Piras M. , Puttonen E. , Shcherbacheva A.
Published: December 2023

Abstract. Forests play a fundamental role in carbon stocking since about a third of the carbon dioxide produced by activities of human origin is absorbed by forests. Forest biomass is an essential indicator of carbon dioxide absorption, enabling an understanding the interaction between forest dynamics and climate change effects. However, biomass and wood material changes are challenging to quantify in forest stands. Nowadays, recent 3D remote sensing technologies, such as laser scanning systems, have allowed accurate measures of single trees. This study evaluates three approaches to classify wood and non-wood materials and quantify biomass based on LiDAR data, aiming at biomass change detection. Specifically, we propose an automated methodology for estimating the single tree-level biomass of a portion of forest monitored through a LiDAR oblique acquisition. The classification of wood and foliage points was performed with machine learning algorithms, while the tree modelling was conducted rigorously through a Quantitative Structure Model (QSM). The purpose of this study is to evaluate (1) two different unsupervised and one semi-supervised classification approaches for wood and foliage separation and (2) the accuracy of the biomass assessment performed on a QSM-based approach on innovative LiDAR acquisitions. The results are promising; the wood-leaf classification performs effectively in all 20 silver birches considered; as regards the biomass, when the noise is limited, it is estimated in a manner consistent with the reference values calculated using an appropriate allometric equation. Higher values are found mainly in dense undergrowth, which negatively affects the modelling of the tree. The research is ongoing, and further tests will be performed to generalize the methodology on different tree species, deepen the multitemporal variability, and improve the accuracy of the assessment.
HYPERSPECTRAL UAS IMAGERY FOR GRASS SWARDS BIOMASS AND NITROGEN ESTIMATION
Authors: Oliveira R. A. , Näsi R. , Korhonen P. , Mustonen A. , Niemeläinen O. , Koivumäki N. , Hakala T. , Suomalainen J. , Kaivosoja J. , Honkavaara E.
Published: December 2023

Abstract. Monitoring agricultural grass fields is particularly important for meat and milk production in Northern Europe, where three harvests occur during a growing season to maximize yields. Reliable data on forage, including biomass and nitrogen concentration, are essential for making informed decisions regarding seed mixtures, fertilizer rates, and harvest timing. Miniaturized hyperspectral cameras mounted on unmanned aerial systems (UAS) have become increasingly accessible and efficient. These cameras, operating in the visible to near-infrared (VNIR) range, have shown potential in estimating grass sward quantity and feeding quality. Additional advancements in hyperspectral technology have emerged the short-wave infrared (SWIR) range for UAS applications, previously utilized mainly in laboratory and aircraft-based systems. This study aims to explore the potential of VNIR and SWIR hyperspectral UAS-based remote sensing in biomass and nitrogen estimation during primary and re-growth stages. Grass fresh yield and nitrogen concentration prediction models were built after selecting the most significant features from the cameras to cope with the high dimensionality of the data. Using best features and machine learning, both fresh yield and nitrogen concentration were estimated with normalized root mean square error better than 10%. This work contributes to the development of accurate remote sensing techniques, supporting sustainable agricultural practices and climate change studies.
EVALUATING CONVNET AND TRANSFORMER BASED SELF-SUPERVISED ALGORITHMS FOR BUILDING ROOF FORM CLASSIFICATION
Authors: Mutreja G. , Bittner K.
Published: December 2023

Abstract. This research paper presents a comprehensive evaluation of various self-supervised learning models for building roof type classification. We conduct linear evaluation experiments for the models pretrained on both the ImageNet1K dataset and a custom building roof type dataset to assess the models’ performance for the roof type classification task. The results demonstrate the effectiveness of the ViT-based BEiTV2 model, which outperforms other models on both datasets, achieving an accuracy of 96.8% from the model pretrained on ImageNet1K dataset and 92.67% on the model pretrained on building roof type dataset. The class activation maps further validate the strong performance of MoCoV3, BarlowTwins, and DenseCL models. These findings emphasize the potential of self-supervised learning for accurate building roof type classification, with the ViT-based BEiTV2 model showcasing state-of-the-art results.