bluuebunny/crossref_metadata_2025 huggingface dataset:
LAND MOVEMENT DETECTION FROM UAV IMAGES
Authors: Pesántez P. C.
Published: May 2022

Abstract. In Reina del Cisne (Cuenca-Ecuador) a dynamic sliding process was created due to a cut that was applied at the beginning of the year 2018 to the hillside without technical considerations for the construction of an access road to a house in the sector. From January 2019 to June 2019, period analyzed in this work, the landslide has caused total structural damage (dwellings near the hillside) or partial (houses away from the hillside) and the total collapse of the path that caused the landslide. The field visits and the comparison with CloudCompare of the clouds of points obtained by flights of an unmanned aerial vehicle, UAV between the months of January 2019 and June 2019, have highlighted the high activity of this landslide. The resulting analysis clearly presents the applicability of this technique for early detection of landslides.
CLOUDTRAN: CLOUD REMOVAL FROM MULTITEMPORAL SATELLITE IMAGES USING AXIAL TRANSFORMER NETWORKS
Authors: Christopoulos D. , Ntouskos V. , Karantzalos K.
Published: May 2022

Abstract. We present a method for cloud-removal from satellite images using axial transformer networks. The method considers a set of multitemporal images in a given region of interest together with the corresponding cloud masks, and delivers a cloud-free image for a specific day of the year. We propose the combination of an encoder-decoder model employing axial attention layers for the estimation of the low-resolution cloud-free image, together with a fully parallel upsampler that reconstructs the image at full resolution. The method is compared with various baselines and state-of-the-art methods on two Sentinel-2 datasets, showing significant improvements across multiple standard metrics used for image quality assessment.
THE SEDUCTION OF THE SIMULATION. 3D MODELLING AND STORYTELLING OF UNREALIZED PERUGIA RAIL STATION
Authors: Bianconi F. , Filippucci M. , Mommi C.
Published: May 2022

Abstract. The study aims at enhancing and requalifying the area of Fontivegge in Perugia. It seeks to instil in the population a sense of identity and belonging to the place through the digital reconstruction of the first design hypothesis of the station itself, transmitting the cultural heritage of the place with new participatory methods, such as serious games and virtual reality. Starting from the project drawings preserved at the academy of San Luca in Rome, conceived by the architect Antonio Cipolla, the project is reconstructed philologically following historical and archival studies, by interpreting the data collected. Following the reconstruction of the 3D model, evocative mook up images and a virtual reality are created, making the intangible tangible and explorable. Through the virtual simulation of the place and its visualisation, new forms of participation are sought, mending the relationship between territory and population, laying the foundations for a different reading of the area and a cultural heritage accessible and open to all.
AUTOMATIC IMAGE-BASED 3D RECONSTRUCTION STRATEGIES FOR HIGH-FIDELITY URBAN MODELS – COMPARISON AND FUSION OF UAV AND MOBILE MAPPING IMAGERY FOR URBAN DESIGN STUDIES
Authors: Wahbeh W. , Müller G. , Ammann M. , Nebiker S.
Published: May 2022

Abstract. Modelling environments for urban projects often involve virtual reconstructions of existing urban areas where generalised city models are not sufficient. The use of photogrammetry with aerial imagery from UAVs on the one hand, and imagery from street-level mobile mapping systems on the other, has advanced significantly in recent years. However, there are limitations when these two imagery types are used separately.The main contribution of this paper is an end-to-end solution for creating large-scale and complex reality-based mesh models of urban environments. We outline a novel image-based 3D reality capturing process by combining street-level imagery from a backpack multi-camera mobile mapping system and nadir and oblique UAV imagery. This reconstruction is based on dense multi-view image matching method. A use case is presented with a goal to reconstruct an area for an urban project which aims to introduce an elevated cycle highway to facilitate traffic. The investigated area has a size of 0.64 square kilometres and is in the city of Basel in Switzerland. The MMS and UAV images were all oriented in a single photogrammetric project. Several reconstructions with different configurations were investigated and the results of the successful reconstruction with a geometric accuracy of a few cm are presented.
TRIPLE EPIPOLAR IMAGES GENERATION AND MATCHING IN TRISTEREO IMAGING ON THE SAME ORBIT MODE
Authors: Yue Q. , Tang X.
Published: May 2022

Abstract. This paper proposed the method to generate triple epipolar images in tristereo imaging on the same orbit mode. It generated projection tie points based on double projection surface in object space, and designed conversion model from epipolar images to its corresponding original images on the basis of projection tie points and created the RPC of epipolar images. During stereo matching one stereo image pairs are used as the basic pair and proposed the rapid transformation model between the current matching points and the corresponding point on the third image, which accomplished the combination of epipolar stereo matching and object space geometric constraints. After left/right bidirectional matching and occlusion area checking, a third matching was implemented. The cost of occlusion area will use the smallest one among the three and these areas were revamped. SGM was used as the optimization strategy. Experiments in three typical areas using Pleiades and ZY-3 as data recourses show that the loss of precision is negligible in the proposed epipolar image generation method. The proposed matching method combines the best of both object geometric constraint method and isolate epipolar image pair method, and the computational efficiency significantly better than these two methods. Lastly, the precision of the proposed method is significantly better than these two methods in building area and weak texture area.
AUTOMATED GENERATION OF HIGH-QUALITY 3D POINT CLOUDS OF ANTLERS USING LOW-COST RANGE CAMERAS
Authors: Cheng S. , Lichti D. D. , Matyas J.
Published: May 2022

Abstract. Three-dimensional imaging demonstrates advantages over traditional methods and has already proven feasible for measuring antler growth. However, antlers' velvet-covered surface and irregular structure pose challenges in efficiently obtaining high-quality antler data. Animal data capture using optical imaging devices and point cloud segmentation still require tedious manual work. To obtain 3D data of irregular biological targets like antlers, this paper proposes an automated workflow of high-quality 3D antler point cloud generation using low-cost range cameras. An imaging system of range cameras and one RGB camera is developed for automatic camera triggering and data collection without motion artifacts. The imaging system enables motion detection to ensure data collection occurs without any appreciable animal movement. The antler data are extracted automatically based on a fast k-d tree neighbor search to remove the irrelevant data. Antler point clouds from different cameras captured with various poses are aligned using target-based registration and the normal distribution transformation (NDT). The two-step registration demonstrates precisions of the overall RMSE of 4.8mm for the target-based method and Euclidean fitness score of 10.5mm for the NDT. Complete antler point clouds are generated with a higher density than that of individual frames and improved quality with outliers removed.
EVALUATION OF THE QUALITY OF REAL-TIME MAPPING WITH CRANE CAMERAS AND VISUAL SLAM ALGORITHMS
Authors: Joachim L. , Zhang W. , Haala N. , Soergel U.
Published: May 2022

Abstract. In the context of the development of an autonomous tower crane, the usage of crane cameras to map the respective workspace as basis for autonomous path planning is investigated. The goal is to generate an up-to-date DEM as input for the crane control. As construction sites are highly dynamic scenes, it is crucial to be able to react to any changes. Thus, real-time mapping with a visual SLAM solution is aspired. As the quality of the DEM is important for such a safety critical application, we are evaluating the mapping quality of four state-of-the-art SLAM solutions, namely ORB-SLAM3, LDSO, DSM and DROID-SLAM. The results show that all approaches can handle our specific crane camera setup and thus are generally suited for our application. The DEM accuracies of all tested methods are even competitive with the result from standard offline photogrammetric processing, at least for the major part of the test site. However, there are limitations with regards to the DEM completeness. Consequently, our investigations show that the tested methods deliver a good basis for real-time accurate mapping, but for their application for autonomous path planning further refinements have to be made.
ACTIVE REINFORCEMENT LEARNING FOR THE SEMANTIC SEGMENTATION OF IMAGES CAPTURED BY MOBILE SENSORS
Authors: Jodeiri Rad M. , Armenakis C.
Published: May 2022

Abstract. In recent years, various Convolutional Neural Networks (CNN) have been used to achieve acceptable performance on semantic segmentation tasks. However, these supervised learning methods require an extensive amount of annotated training data to perform well. Additionally, the model would need to be trained on the same kind of dataset to generalize well for other tasks. Further, commonly real world datasets are usually highly imbalanced. This problem leads to poor performance in the detection of underrepresented classes, which could be the most critical for some applications. The annotation task is time-consuming human labour that creates an obstacle to utilizing supervised learning methods on vision tasks. In this work, we experiment with implementing a reinforced active learning method with a weighted performance metric to reduce human labour while achieving competitive results. A deep Q-network (DQN) is used to find the optimal policy, which would be choosing the most informative regions of the image to be labelled from the unlabelled set. Then, the neural network would be trained with newly labelled data, and its performance would be evaluated. A weighted Intersection over Union (IoU) is used to calculate the rewards for the DQN network. By using weighted IoU, we target to bring more attention to underrepresented classes.
EVALUATION OF SELF-SUPERVISED LEARNING APPROACHES FOR SEMANTIC SEGMENTATION OF INDUSTRIAL BURNER FLAMES
Authors: Landgraf S. , Kühnlein L. , Hillemann M. , Hoyer M. , Keller S. , Ulrich M.
Published: May 2022

Abstract. In recent years, self-supervised learning has made tremendous progress in closing the gap to supervised learning due to the rapid development of more sophisticated approaches like SimCLR, MoCo, and SwAV. However, these achievements are primarily evaluated on common benchmark datasets. In this paper, we focus on evaluating self-supervised learning for semantic segmentation of industrial burner flames. Our goal is to build an intuition on how self-supervision performs in a scenario relevant for industrial application where training labels and the opportunities for hyperparameter tuning are limited. We demonstrate that self-supervised pre-training can constitute an alternative to the state-of-the-art approach of pre-training on ImageNet. Across all scenarios, the self-supervised approaches are less susceptible to sub-optimal learning rates and achieve higher mean accuracies than ImageNet pre-training, especially when training labels are scarce.
RESAMPLING METHODS FOR A RELIABLE VALIDATION SET IN DEEP LEARNING BASED POINT CLOUD CLASSIFICATION
Authors: Nurunnabi A. , Teferle F. N.
Published: May 2022

Abstract. A validation data set plays a pivotal role in tweaking a machine learning model trained in a supervised manner. Many existing algorithms select a part of available data by using random sampling to produce a validation set. However, this approach can be prone to overfitting. One should follow careful data splitting to have reliable training and validation sets that can produce a generalized model with a good performance for the unseen (test) data. Data splitting based on resampling techniques involves repeatedly drawing samples from the available data. Hence, resampling methods can give better generalization power to a model, because they can produce and use many training and/or validation sets. These techniques are computationally expensive, but with increasingly available high-performance computing facilities, one can exploit them. Though a multitude of resampling methods exist, investigation of their influence on the generality of deep learning (DL) algorithms is limited due to its non-linear black-box nature. This paper contributes by: (1) investigating the generalization capability of the four most popular resampling methods: k-fold cross-validation (k-CV), repeated k-CV (Rk-CV), Monte Carlo CV (MC-CV) and bootstrap for creating training and validation data sets used for developing, training and validating DL based point cloud classifiers (e.g., PointNet; Qi et al., 2017a), (2) justifying Mean Square Error (MSE) as a statistically consistent estimator, and (3) exploring the use of MSE as a reliable performance metric for supervised DL. Experiments in this paper are performed on both synthetic and real-world aerial laser scanning (ALS) point clouds.
GUIDED CALIBRATION OF MEDICAL STEREO ENDOSCOPES
Authors: Hardner M. , Docea R. , Schneider D.
Published: May 2022

Abstract. Using stereo endoscopes for 3D reconstruction in minimally invasive surgery, the calibration of the stereo system is required. Since calibration in general and of medical endoscopes in particular is a well discussed topic, this publication will focus on the user experience in the operating room. To enable the medical personnel in performing the calibration, a guided process using an augmented camera image has been developed and implemented on the basis of the Robot Operating System framework. A similarly guided accuracy check allows the user to determine beforehand if a calibration is necessary.The method is tested by performing multiple calibrations on two different stereo endoscopes. It is shown, that the accuracy check and calibration can be completed in around 2 minutes. The resulting calibration parameters for one endoscope are analyzed in terms of temporal stability. Additionally, the self-heating that occurs after the system starts is examined. It is shown that short- and long-term effects impact the stereo system stability and that regular re-calibrations might be required for use in 3D reconstruction.
3D MODELS AND NON-DESTRUCTIVE INVESTIGATIONS: TOWARDS A MEETING IN DIGITAL TWINS
Authors: Patrucco G. , Perri S. , Sammartano G. , Fillia E. , Matteini I. , Lenticchia E. , Ceravolo R. , Spanò A.
Published: May 2022

Abstract. The digitisation projects of architectural heritage are a field of research continuously evolving and updating in parallel to technological innovations that allow to progressively boost the challenges and requirements of geometric and semantic richness of the related digital configuration of results. Among the arduous tasks under observation, there is the increasing need to harmonize and make the spatial and heterogenous information aspects cooperating. This paper focuses on the procedures for structuring an information system based on the as-built modelling strategy applied on a particularly relevant example of reinforced concrete architecture, which presents the bearing structure of the structural elements featured by complex geometries and roofing conceived as ferrocement wavy elements and therefore equally unusual as a construction system. The entire project refers to a conservation plan that involves structural non-destructive investigations that inquiry the state of health of the structures and their seismic performance. The purpose is to structure a parametric system, based on HBIM technology, which enables the archiving of investigations and results in order to strengthen direct spatial reference between geometric elements and structural analysis results, based also on image and range-based surface analyses, in the direction of a digital twin of the architectural complex.
DEVELOPING AN EDUCATIONAL CULTURAL HERITAGE 3D PUZZLE IN A VIRTUAL REALITY ENVIRONMENT
Authors: Roumana A. , Georgopoulos A. , Koutsoudis A.
Published: May 2022

Abstract. The ways our cultural heritage reserve is preserved and disseminated to the public have changed significantly, with the use of immersive technologies, such as virtual reality environments, and serious games. Nowadays, these technologies are also exploited for developing interactive informative applications, to support historical education and enhance museum visits, physical or virtual, especially to younger generations. The field of edutainment, educational entertainment, has been rapidly developing during the last 10 or 15 years. The main goal of this research is to develop an educational 3D puzzle-like serious game which can operate within a virtual reality environment while aiming towards the dissemination of cultural heritage content to the younger public, i.e., students, children etc., through a pleasant gamification process. The cultural heritage objects used are an ancient Greek temple and a statue of the Roman era, whose high-resolution fully textured 3D models were available from previous projects. The game application was developed in Unity game engine with suitable coding to enable the smooth execution of the 3D puzzle solution. The application verified that it is more interesting to learn about cultural assets through a game than in the conventional ways, and even more when it is implemented within a Virtual Reality environment, where the contact with the assets appears to be more direct and realistic. The same application can also be utilized in different educational areas and can be expanded by the inclusion of other digital assets.
MODELLING COLOUR ABSORPTION OF UNDERWATER IMAGES USING SFM-MVS GENERATED DEPTH MAPS
Authors: Vlachos M. , Calantropio A. , Skarlatos D. , Chiabrando F.
Published: May 2022

Abstract. The problem of colour correction of underwater images concerns not only surveyors, who primarily use images for photogrammetric purposes, but also archaeologists, marine biologists, and many other domains experts whose aim is to study objects and lifeforms underwater. Different methods exist in the literature; some of them provide outstanding results but works involving physical models that take into account additional information and variables (light conditions, depths, camera to objects distances, water properties) that are not always available or can be measured using expensive equipment or calculated using more complicated models. Some other methods have the advantages of working with basically all kinds of dataset, but without considering any geometric information, therefore applying corrections that work only in very generic conditions that most of the time differs from the real-world applications.This paper presents an easy and fast method for restoring the colour information on images captured underwater. The compelling idea is to model light backscattering and absorption variation according to the distance of the surveyed object. This information is always obtainable in photogrammetric datasets, as the model utilises the scene's 3D geometry by creating and using SfM-MVS generated depth maps, which are crucial for implementing the proposed methodology. The results presented visually and quantitatively are promising since they are an excellent compromise to provide a straightforward and easily adaptable workflow to restore the colour information in underwater images.
INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN
Authors: Chen Y. K. , Lin Y. T. , Yen H. Y. , Chang N. H. , Lin H. M. , Yang K. H. , Chen C. S. , Wang L. P. , Cheng H. K. , Wu H. H. , Han J. Y.
Published: May 2022

Abstract. Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction.
DISASTER DAMAGE INVESTIGATION USING ARTIFICIAL INTELLIGENCE AND DRONE MAPPING
Authors: Kim S. S. , Shin D. Y. , Lim E. T. , Jung Y. H. , Cho S. B.
Published: May 2022

Abstract. This study aims to testify the applicability of UAV photogrammetry and artificial intelligence (AI) for the management of natural disaster. Recently artificial intelligence is considered as an emerging tool for recognizing disaster events from aerial imagery of drones. In this paper, we present firstly the approach related to use of AI techniques for disaster detecting and identification. Secondly, we suggest small easy-to-use UAV-based investigation procedure for natural disaster damaged area in the phase of disaster recovery in Korea. Finally, we evaluate the mapping accuracy and work efficiency of drone mapping for disaster investigation application through comparing with traditional investigation work process which was dependent on labor-intensive field survey. The resolution ortho-image map of within less 5cm of GSD generated by aerial photos acquired from UAVs at the altitude of 100m–250m enabled us to check damage information such as facilities destroy or the trace of soil erosion around the river flooded and reservoir collapsed area. The photogrammetry-based drone mapping technology for the disaster damage investigation is expected to be an alternative approach to support or replace the labor-intensive disaster site survey that needs to investigate the disaster site quickly and timely.
THE POTENTIAL CARBON LOSSES ESTIMATION WITH REMOTE SENSING-BASED DATA: CASE STUDY IN NOVA VIDA RANCH, RONDONIA, BRAZIL
Authors: Lyu L. , Shen J.
Published: May 2022

Abstract. Carbon losses have enormous impacts on ecosystem and indicative meaning for ecological stability, which are, therefore, necessary to be evaluated. In recent years, with the increasingly number of people settling in Amazon basin, large areas of tropical forests have been exploited, which took the mainly responsible for local deforestation.Carbon losses are hardly to be evaluated directly but can be inferred from the relationship between potential carbon and forest structures referring to previous studies. Remote sensing-based methods are widely used because of the obvious advantages of, such as the repeatability of data collection, high correlations between spectral bands and vegetation parameters etc. However, the selection of the spatial resolution of data is a critical issue that should be taken into consideration for it influences the performance of image texture thereby the discrimination of land covers, especial in complex forest stand structure. The approach for image feature extraction used in the study is explained.Based on the available datasets, this study aims to estimate the potential carbon losses across selected plot in Nova Vida Ranch, Rondônia, Brazil between 1989 and 2001. The results showed that carbon potentials decreased at least 1.1*108 (Kg C) during the 12 years, which would affect all the climate, wildlife habitats and biodiversity. The study suggested that it is significantly important to reduce the uncertainties that are caused by saturation in some of the tropic areas. To pursue this, the research on the integration of optical and radar data is worth to experiment in further study.
COMPARISON OF SATELLITE IMAGE DENOISING TECHNIQUES IN SPATIAL AND FREQUENCY DOMAINS
Authors: Oguzhanoglu S. , Kapucuoglu I. , Sunar F.
Published: May 2022

Abstract. In recent years, remote sensing images have been used for many different applications that require visual analysis and interpretation. In this paper, reducing/removing noise is the basic approach, as it causes loss of information and therefore affects the accuracy of the analyses. Within the scope of the study, two different test areas of land cover/use were applied to examine the effects of noise on optical satellite images. In this context, Landsat 8 and Sentinel 2 satellites were used to study the effects of denoising methods on different spatial resolutions. Due to the lack of raw images of the selected satellites, two different types of noise (i.e. Gaussian and Stripe) were added to the images. In this context, four different denoising methods were compared by using conventional filter techniques commonly used in the spatial domain, while also different methods that used different threshold values in the frequency domain. The first approach is Median, Block Matching and 3D Filtering methods in the spatial domain, applications that depend mainly on the neighborhood relationship of pixels in the image. The second approach is wavelet-based Contourlet and Curvelet methods in the frequency domain. The quality analysis of denoised images were evaluated as qualitative (statistical methods Peak Signal to Noise Ratio, Mean Square Error, standard deviation, min/max value), and quantitative. Finally, Curvelet hard thresholding transform was the selected method as the best algorithm after quality analysis additionally, the method also effectively preserves edges in homogeneous test area and other fine details in the heterogeneous test area.
RESEARCH ON THE METHOD OF USING MULTI-SOURCE DATA TO EVALUATE THE QUALITY OF GEOGRAPHICAL NAMES
Authors: Xu Y. , Zhang J. , Wang X. , Zhao H. , Chen C. , Mao W.
Published: May 2022

Abstract. Based on the epidemic situation in recent years, it has become an urgent need to study how to use many open-source or existed geographical names to evaluate the quality of newly produced geographical names. By analysing the quality characteristics of geographical names data, we can find the key quality elements of geographical names data. We obtained a method of using multi-source data to evaluate newly produced geographical names. This method based on adaptive weight distribution of multi-source data. This method can not only evaluate the whole quality of geographical names, but also grade the specific quality of geographical names. The experiment shows that this method is effective and has strong reference for users and producers.
ENVIRONMENTAL IMPACT ASSESSMENT OF PIT LATRINES USING REMOTE SENSING AND MULTI-CRITERIA SPATIAL ANALYSIS IN ULAANBAATAR (GER AREA), MONGOLIA
Authors: Oyunbat P. , Munkh-Erdene A. , Enkhbayar B. , Batkhishig O. , Myagmartseren P.
Published: May 2022

Abstract. This study aimed to analyze the environmental impact of pit latrines of ger-sprawl district areas (low rise sprawl area with detached “ger”- nomad’s mobile tent surrounded by fences or with handmade dwellings) in Ulaanbaatar capital city of Mongolia using remotely sensed data and multi-criteria spatial analysis. Within the last two decades, the residential areas without infrastructure have been significantly expanded and changed due to the migration of people from rural areas. 50.1% of the total population of Ulaanbaatar city is lives in ger district areas. Due to the formation of unstructured residential areas, pollution from pit latrines poses a risk to the urban environment and human health by infiltrating shallow water, being carried by floodwaters, and being released into the air. Therefore, to reduce the number of pit latrines in ger areas, the total number and location of pit latrines in the city were determined using high-resolution satellite imagery. In addition, spatial multi-criteria analysis methods were used to assess the negative impact of pit latrines on the environment and human health and categorize them into four zones. A total of 144,992 pit latrines were counted in Ulaanbaatar, with 9,443 pit latrines in Zone IV, 43,377 pit latrines in Zone III, 71150 pit latrines in Zone II, and 21,022 pit latrines in Zone I. The results of this study are important for implementing management measures to reduce soil pollution, such as restricting and banning the use of pit latrines in ger areas, installing toilets that meet MNS 5924: 2015 standards, and carrying out disinfection.
SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?
Authors: Marszalek M. L. , Le Saux B. , Mathieu P.-P. , Nowakowski A. , Springer D.
Published: May 2022

Abstract. Machine learning, satellites or local sensors are key factors for a sustainable and resource-saving optimisation of agriculture and proved its values for the management of agricultural land. Up to now, the main focus was on the enlargement of data which were evaluated by means of supervised learning methods. Nevertheless, the need for labels is also a limiting and time-consuming factor, while in contrast, ongoing technological development is already providing an ever-increasing amount of unlabeled data. Self-supervised learning (SSL) could overcome this limitation and incorporate existing unlabeled data. Therefore, a crop type data set was utilized to conduct experiments with SSL and compare it to supervised methods. A unique feature of our data set from 2016 to 2018 was a divergent climatological condition in 2018 that reduced yields and affected the spectral fingerprint of the plants. Our experiments focused on predicting 2018 using SLL without or a few labels to clarify whether new labels should be collected for an unknown year. Despite these challenging conditions, the results showed that SSL contributed to higher accuracies. We believe that the results will encourage further improvements in the field of precision farming, why the SSL framework and data will be published (Marszalek, 2021).
EVALUATION OF EARLY SEASON MAPPING OF INTEGRATED CROP LIVESTOCK SYSTEMS USING SENTINEL-2 DATA
Authors: Toro A. P. S. G. D. , Werner J. P. S. , Dos Reis A. A. , Esquerdo J. C. D. M. , Antunes J. F. G. , Coutinho A. C. , Lamparelli R. A. C. , Magalhães P. S. G. , Figueiredo G. K. D. A.
Published: May 2022

Abstract. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover.
BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA
Authors: Yadav R. , Nascetti A. , Ban Y.
Published: May 2022

Abstract. Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation without losing necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: ‘newly built’, ‘demolished’, ‘taller’ and ’shorter’. The detected changes are visualized in one map for better interpretation.
GRAPH NEURAL NETWORK BASED OPEN-SET DOMAIN ADAPTATION
Authors: Zhao S. , Saha S. , Zhu X. X.
Published: May 2022

Abstract. Owing to the presence of many sensors and geographic/seasonal variations, domain adaptation is an important topic in remote sensing. However, most domain adaptation methods focus on close-set adaptation, i.e., they assume that the source and target domains share the same label space. This assumption often does not hold in practice, as there can be previously unseen classes in the target domain. To circumnavigate this issue, we propose a method for open set domain adaptation, where the target domain contains additional unknown classes that are not present in the source domain. To improve the model’s generalization ability, we propose a Progressive Weighted Graph Learning (PWGL) method. The proposed method exploits graph neural networks in aggregating similar samples across source and target domains. The progressive strategy gradually separates the unknown samples apart from known samples and upgrades the source domain by incorporating the pseudolabeled known target samples. The weighted adversarial learning promotes the alignment of known classes across different domains and rejects the unknown class. The experiments performed on a multi-city dataset show the effectiveness of the proposed approach.
EFFECTS OF URBAN GREENERY ON HEALTH. A STUDY FROM REMOTE SENSING
Authors: Arellano B. , Roca J.
Published: May 2022

Abstract. Global warming is causing increasing Heat Waves that affect human health. High temperatures markedly increase morbidity and mortality. Urban Heat Islands increase the effects of Heat Waves and are a serious inconvenience to human health and comfort. Cities can substantially increase local temperatures and reduce temperature drop at night. During the night, the greater thermal inertia of the central areas reduces their cooling capacity. On the other hand, it is important to highlight that urban vegetation plays a key role in adapting cities to Global Warming and Urban Heat Island. Green areas have lower temperatures than the rest of land uses and generate a cooling effect that spreads to their surroundings creating a "cool island" effect. The main objective of this paper is to establish the nocturnal land surface temperature and land surface air temperature of Barcelona Metropolitan Area (35 municipalities, 636 km2, 3.3 million inhabitants) in an episode of a nocturnal heatwave and to estimate its possible impact on health and mortality. Subsequently, nighttime temperatures are analysed in this extreme heat context to determine their spatial distribution and detect the urban landscapes that are most vulnerable to extreme night heat. Modelling of land surface temperature must reveal the elements that determine night Urban Heat Island and consequently identify actions that can be implemented at urban planning level to refresh the environment during the night and thus increase the resilience of the most vulnerable landscapes and improve residents’ health. This paper studies the effect of urban greenery and green infrastructures on Nighttime Urban Heat Island and propose climate adaptation measures and design for urban green areas to decrease high temperature in a Heat Wave context, which contributes to reducing the serious negative impacts on people's health.
EVALUATION OF DEM DERIVED BY REPEAT-PASS X-BAND STRIPMAP MODE PAZ DATA
Authors: Abdikan S. , Bayik C. , Calò F. , Pepe A. , Balik Sanli F.
Published: May 2022

Abstract. This paper, presents the initial results of digital elevation model (DEM) extraction from PAZ Synthetic Aperture Radar (SAR) satellite images using repeat-pass interferometric analysis. We used a multi-temporal high-resolution strip-map mode X-band satellite image that has a single polarization. Five main classes, i.e., volcanic structures, agriculture, settlement, sand dune and plain bareland are considered depending on the structure of the region. Within the category, the coherence value and DEM value are evaluated. In the accuracy assessment analysis, a reference map produced from aerial photogrammetry is used. Additionally, global DEM TanDEM-X data is also tested in the study region. In the analysis, quality metrics, mean error (ME), root means square error (RMSE), standard deviation (STD), and the normalized median absolute deviation (NMAD) are used. The results showed that as the temporal baseline increases the coherence values and the quality of the DEM product decrease. The RMSE values range between 2.36 m to 7.09 m in different classes. The TanDEM-X data provided high accuracies over each class range from 0.88 m to 2.40 m. Since the study area is vulnerable to sinkhole formation, sinkhole-like signals were also observed in the interferograms obtained from different and sequential pairs. The high-resolution repeat-pass PAZ data pointed out its potential for interferometric products generation.
ASSESSING LEVELLING AND DINSAR FOR DEFORMATION MONITORING IN SEISMIC REGION
Authors: Di Stefano F. , Cuevas-Gonzalez M. , Luzi G. , Malinverni E. S.
Published: May 2022

Abstract. Carrying out monitoring surveys in seismic regions is good practice both for the assessment of land deformation and the evaluation of building structures standing on it. In this work, topographic levelling and DInSAR techniques have been used for displacement measurement. These geomatic techniques are rarely applied in the same context and attempts are made to combine the results obtained for having a complete analysis of the site. The proposed work analyses, compares and discusses topographic levelling and advanced multi-temporal DInSAR techniques used to detect and measure ground deformation when the occurrence of seismic events might have played a role in the displacement. The area of interest had already been under observation through ground-based monitoring surveys, by means of metal bolts attached to façades of buildings detected by topographic level, from 1998 to 2021. The DInSAR analysis was carried out exploiting Sentinel-1A/B data acquired during the period 2014–2021. The goal of the DInSAR processing stage of the procedure is to derive the deformation map of the area of interest from SAR data. A zero date has been set for both survey methods in order to define similar time series for comparison analysis. The results showed that ground displacements measured by levelling and DInSAR have similar trends. On the geomorphological aspect, the same distribution map of terrain subsidence is found in both techniques.
INTERFEROMETRIC SAR DEFORMATION MONITORING USING PASSIVE REFLECTORS AND ASCENDING AND DESCENDING PASSES
Authors: Kotulak N. , Mleczko M. , Crosetto M. , Palamà R. , Mróz M.
Published: May 2022

Abstract. The article concerns the estimation of land deformation in an area of copper mining using the Persistent Scatterer Interferometry (PSI) technique and C-band data coming from the Sentinel-1 satellite. The aim of the work is a comparison of two different PSI approaches exploiting passive corner reflectors deployed pairwise, oriented towards the satellites in ascending and descending orbits. The comparison was done in the line of sight geometry. In addition, a validation was carried out using GNSS measurements. The results achieved using the two PSI approaches are consistent and mutually coherent. They are also congruent with observations from GNSS stations. These results proves that the method proposed to identify pixels on multi-looked images is correct. In addition, the intermediary results on the corner reflectors shows that their responses, in terms of dispersion of amplitude and the coherence, is slightly different. This needs to be further studied.
ANALYSIS OF MINING-INDUCED TERRAIN DEFORMATION USING MULTITEMPORAL DISTRIBUTED SCATTERER SAR INTERFEROMETRY
Authors: Palamà R. , Crosetto M. , Monserrat O. , Barra A. , Crippa B. , Mróz M. , Kotulak N. , Mleczko M. , Rapinski J.
Published: May 2022

Abstract. This work addresses a methodology based on the Interferometric Synthetic Aperture Radar (InSAR) to analyse and monitor ground motion phenomena induced by underground mining activities, in the Legnica-Glogow Copper District, south-western Poland. Two stacks of ascending and descending Sentinel-1 Synthetic Aperture Radar (SAR) images are processed with a small baseline multitemporal approach. A simple method to select interferograms with high coherence and eliminated images with low redundancy is implemented to optimize the interferogram netwrork. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against Global Navigation Satellite System (GNSS) measurements.
DIFFERENTIAL SAR INTERFEROMETRY FOR THE MONITORING OF LAND SUBSIDENCE ALONG RAILWAY INFRASTRUCTURES
Authors: Wassie Y. , Gao Q. , Monserrat O. , Barra A. , Crippa B. , Crosetto M.
Published: May 2022

Abstract. The paper summarizes the results of an ongoing research project carried out in cooperation between CTTC (Spain) and the University of Milan (Italy). The work aimed at investigating the role of quality indicators in the analysis of differential interferometric SAR time series products. Small baseline multi-temporal differential interferometric techniques have been used to derive TS products from six-year Sentinel-1 images covering railway networks in Barcelona, Spain. Redundancies of interferograms and post-phase unwrapping phase estimation residuals were pivotal parameters in determining the reliabilities of measurements. Preliminary results have supported the importance of quality indicators as well as the feasibility of multi-temporal differential interferometric techniques in monitoring subsidence along railway infrastructures. The time series evolutions of measurements from coherent scatterers have also shown that the target area is stable in the study period.
LAND SUBSIDENCE MONITORING AND ANALYSIS IN FUZHOU BASED ON INSAR AND MULTISPECTRAL REMOTE SENSING TECHNOLOGY
Authors: Zhu Y. Y. , He Y. F. , Li H. Y. , Lv Z. P. , Xu G. C.
Published: May 2022

Abstract. Interferometric Synthetic Aperture Radar (InSAR) technology has millimeter level measurement accuracy and has great advantages in urban land subsidence monitoring. Meanwhile, multispectral remote sensing technique can also provide a large amount of urban features changes information for analyzing the causes of land subsidence. In this study, SAR and multispectral images are both used to monitor and analyze land subsidence in Fuzhou of China. 115 scenes Sentinel-1 SAR images from May 2017 to May 2021 are used based on the Persistent Scatterers Interferometric (PSI) method to evaluate the land subsidence in Fuzhou, while Sentinel-2 multispectral images are used to evaluate several remote sensing indexes. During SAR data processing, Generic Atmospheric Correction Online Service for InSAR (GACOS) data is used to remove atmospheric errors for higher accuracy land subsidence. In order to analyze the relationship between the land subsidence and land cover changes in urban areas, the Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) and Modified Normalized Difference Water Index (MNDWI) of the main subsidence areas are obtained based on Sentinel-2 multispectral images from 2016 to 2021. In the end, it is found that the land subsidence in some areas exceeded 12 mm/year in Fuzhou. The time series of four areas with severe subsidence were analyzed, and the cumulative subsidence reached about 60 mm. Besides, the spatial distribution and temporal changes of vegetation, buildings and water bodies in these areas were obtained based on the multispectral data, it is found there is very less relationship between the land subsidence and the urban features. It is concluded that that the main causes of the land subsidence are the changes of land internal components such as groundwater and others.
RETRIEVAL OF LEAF AREA INDEX AND LEAF CHLOROPHYLL CONTENT FROM HYPERSPECTRAL DATA USING DEEP LEARNING NETWORKS
Authors: Hu B. , Jung W. M. , Liu J. , Shang J.
Published: May 2022

Abstract. This study aimed to exploit the use of deep learning networks in the retrieval of the biophysical and biochemical parameters of vegetation canopies. Convolutional Neural Network (CNN), network with only fully connected layers, referred as dense network (DNN), and Autoencoder (AE) were investigated to retrieve leaf area index (LAI) and leaf chlorophyll content. Hyperspectral data simulated by the coupled PROSPECT and SAIL model were used for training and validation. The real CASI hyperspectral data in 50 spectral channels ranging from 522.4 nm to 894.2 nm collected over three agricultural crop fields during the growing season of 2001 were used, together with the in-situ measured LAI and leaf chlorophyll content, as independent test set. Occlusion analysis was also employed to determine the important spectral bands at which reflectance made more contributions to the retrieval with a CNN and interpret the latent variables of the AE. Satisfactory results from these deep learning networks were obtained, compared with ground truth. The DNN with the input of the vegetation indices sensitive to LAI and leaf chlorophyll content (MTVI2 and TCARI/OSAVI) generated the best results with R2 of 0.86 for LAI and 0.55 for leaf chlorophyll content.