bluuebunny/crossref_metadata_2025 huggingface dataset:
Emotion in Art Therapy as Creative Thinking in the Psychological Aspect
Authors: Velayutham Vivek , Kannuswamy Raman
Published: December 2023

Many have found relief from their inner turmoil by using art therapy. Blending art therapy with introspective contemplation allows one to get a fresh understanding of one's innermost senses. Art therapy uses an artistic approach over the creative toolkits and fosters a more intuitive approach by trying new mediums, methods, and hues. Art therapy has been demonstrated to alleviate stress, sadness, and anxiety. Group art therapy is a type of art therapy that is conducted in a group setting. This type of therapy can be particularly beneficial for people struggling with social anxiety or struggling to express their emotions in a group setting. Problem-solving and critical thinking are only a few creative talents that might benefit from art therapy. Individuals may grow their imagination and learn to approach problems by tinkering with various methods. This study reflects the gain in self-confidence and optimising the psychological complications using art therapy.
Switching Heads and Cultures: Transformation of an Indian Myth into the Quest of Identity Crisis in Girish Karnad’s Hayavadana
Authors: Ghosh Subho
Published: December 2023

Myths and legends have an enduring significance for the fundamental human obsessions. Indian drama is flavoured with several elements in which mythology plays an important role. One of the most renowned playwrights of contemporary India, Girish Karnad who has made use of the theatre to explore the culture of Indian past, re-created Hayavadana, a commentary on the problems of human being’s search for completeness and perfection, with his own imaginative calibre deriving it from Kathasaritsagar, an 11th century compendium of Indian legends and drawing the picturesque of Thomas Mann’s story of The Transposed Heads in a modern approach. It revolves around the triangular relation between Padmini, Devdatta and Kapila ending in Padmini’s frustrated search to get the best of both men in one body highlighting the confusion of human identities and the ambiguous nature of human personality. This present paper focuses on how Girish Karnad re-interprets the mythology in his creation rummaging a conflict between modernity and indigenous tradition against the backdrop of mythical history to enunciate the acute crisis of identity, self-discovery and imperfection or and wholeness to reach to the zenith of perfection that is next to impossible reflecting the strength of psychology over the physical one where the human beings strive to obtain perfection in all the aspects throughout their life but they forget the fact that perfection is something that is unachievable. It is concluded that although the ending of the play is not within the orthodoxy of Indian epic texts and Hindu philosophy, it can be seen in the cultural context of an Indian woman of today who seeks to fulfil her needs and aspirations.
Divergent Perspectives: USCIRF vs. Public Opinion on Religious Freedom in India
Authors: Mukhopadhyay Subhodeep
Published: December 2023

The United States Commission on International Religious Freedom (USCIRF), a U.S. government body, has expressed significant concerns about the state of religious freedom in India in recent years. It has particularly highlighted what it alleges to be a decline in religious freedom, especially since the Bharatiya Janata Party (BJP) assumed power in May 2014. The USCIRF’s annual reports claim that Hindu groups routinely orchestrate violence against Muslims, Christians and other minorities, often with the tacit support of the Indian State and its various organs. This paper examines the commission’s claims regarding worsening religious freedom in India, specifically examining the alignment of these claims with public opinion on the matter. A quantitative analysis of 19 years' worth of Google Trends data is conducted to evaluate if public opinion on religious freedom has significantly worsened, since the BJP came to power. The study examines 9 specific dimensions of religious freedom in India. Results show that 8 out of the 9 dimensions display a statistically significant decline in search volumes after mid-2014. In these 8 cases, the average decline in web searches is more than 75%, indicative of a sharp relative decrease in perception of worsening religious situation in India, which is quite contrary to the USCIRF’s allegation of increasing religious intolerance.
Education: Then and Now - "A Comparative Study of Teaching Tools and Methodology"
Authors: Banerjea Kingshuk , Das Soumita
Published: December 2023

This paper focuses on the paradigm shift of teaching-learning process and overall teaching methodology after Covid19 has hit the world. This paper shows the traditional and digital teaching-learning procedure and the challenges that are faced in Indian context. The education world for ages has been seasoned in a particular way of teaching-learning. The conventional teaching procedure followed certain rules which formed the doctrine of education system. The change though started long time back in 1960s, yet this was not totally acceptable as an effective method of teaching-learning. Conventional method still ruled the world and non-conventional (online) was considered as a distant substitute mode of learning. The entire world saw a paradigm shift in the education due to the pandemic that cornered every individual across the world. Blackboard changed to smart board and jam board or even PowerPoint presentation. Class rooms changed to Zoom Calls, Webex meeting, Google meet, Teams etc. Face to face discussion changed to online forums, Books changed to internet and YouTube. The shift is prominent. Though this was needed, yet it leads to noticeable changes in our society where many perceive online education as an obstacle to teaching-learning process. The digital divide in all the third-world nations has made this process one-sided. This has created a sociological divide.
Executive Legislation in India: Legality of Ordinance Making Power
Authors: Varshney Dr. Rahul , Sharma Sachin
Published: December 2023

Misuse of ordinance making power by executive is one of the major problems of parliamentary democracy in India. Indian constitution provisioned for ordinance to tackle the urgent situations created through exceptional circumstances but it has been exercised to trespass into the legislative functions of the state. In many cases legislature was replaced with the executive as many ordinances were re-promulgated repeatedly for years without even bringing before the legislature. This paper tries to analyze the basic traits of ordinance making power and its current practice by the different governments in state and center. It also summarizes the judicial attitude towards ordinance making power and its efforts to curb its abuse by scrutinizing legality of ordinances time to time.
The Study of an analytical interpretation of the Buddha’s midnight preaching: Special reference to pāli canonical literature
Authors: Vilasalankara Vilasalankara
Published: December 2023

The Dhamma that Lord Buddha had preached to the people of that time for 45 years, was later compiled in the form of three Pitakas, called Vinaya, Suttanta, and AbhiDhammapitaka. Among them, the Vinayapitaka is the name given to a collection of the various Sikkhāpada, or precepts contained, beginning with the Pārājikasand ending with the seventy-five SekkhiyaDhamma. These precepts are solemnly read twice a month in every monastery. It contains two kinds of collections of precepts for monks and nuns, and of classification (Vibhanga), twenty-two divisions (Khandhaka), and sixteen acts of surrounding (Parivāra) contained. The second pitaka is Suttantapitaka and the third one is known as AbhiDhammapitaka. Buddha’s inner life was one of meditation and the experience of Nibbnasukha, while his outer life was one of selfless service for the moral upliftment of the world. He enlightened, and He did His best to enlighten others and release them from the miseries of life.After attaining enlightenment, Tathagata gave discourses on many occasions. He has given some discourses during the day, some in the afternoon, some in the evening and some at night. This paper mainly studies the analytical interpretation of the Suttas given by Lord Buddha at the time of midnight.
A IMPORTÂNCIA DA PROTEÇÃO DOS ANIMAIS DOMÉSTICOS NO ORDENAMENTO JURÍDICO
Authors: Silva Vitória Santos da
Published: December 2023

A proteção dos animais domésticos no ordenamento jurídico é um tema de crescente relevância, refletindo o compromisso da sociedade em garantir o bem-estar desses seres. O estudo tem como objetivo principal analisar a eficácia das leis e regulamentos existentes no Brasil para a proteção desses animais domésticos, avaliando seu impacto na prevenção de abusos e maus-tratos, para alcançar esse objetivo, foi conduzida uma pesquisa abrangente, que inclui análise de legislação vigente e estudos de casos. Além disso, foram examinadas as implicações éticas e sociais da proteção dos animais domésticos. Os resultados obtidos revelam que embora o Brasil tenha leis específicas para a proteção dos animais, sua aplicação e eficácia variam em todo o país. A fiscalização muitas vezes é insuficiente, levando casos frequente de abuso e negligência. Além disso, a conscientização pública ainda é limitada, contribuindo para a perpetuação dos problemas. Com base no exposto, conclui-se que é essencial aprimorar a legislação existente e fortalecer aos mecanismos de fiscalização. Além disso, a educação e a conscientização da sociedade desempenham um papel fundamental. É imperativo que os legisladores, as autoridades e a sociedade civil trabalhem em conjunto para assegurar um ambiente mais seguro e compassivo para os animais domésticos no Brasil.
UAV LARGE OBLIQUE IMAGE GEO-LOCALIZATION USING SATELLITE IMAGES IN THE DENSE BUILDINGS AREA
Authors: Luo J. , Ye Q.
Published: December 2023

Abstract. For UAV large oblique image geo-localization in the dense buildings area, there are still two main challenges. One is the presence of obvious occlusion and large viewpoint differences in UAV images, and the other arises from the fact that reference images, particularly orthographic satellite images, lack façade information of man-made structures (such as buildings and roads), which is crucial for UAV large oblique images. Most of existing image-based geo-localization methods only address the first challenge, neglecting the interference brought by the second challenge, especially for UAV large oblique image geo-localization in the dense buildings area. Motivated by both these two challenges, we have proposed a novel method for UAV large oblique image geo-localization in the dense buildings areas, with the segments direction statistics (SDS) features and their histogram descriptors designed. By considering both the local and global features of man-made structures, the proposed method effectively addresses the significant information difference encountered in cross-view image matching. We conducted experiments on both the public UAV images dataset University-1652 and our own collected dataset of UAV large oblique long focal whiskbroom (LO-LF-W) images. Comparative analysis with state-of-the-art (SOTA) methods demonstrated that the proposed method improves the geo-localization accuracy by approximately 10%. Furthermore, the proposed method exhibits greater robustness to noise and changing orientation of reference images, making it particularly well-suited for dense buildings areas that pose challenges for existing methods.
CONTINUOUS BIM ALIGNMENT FOR MIXED REALITY VISUALISATION
Authors: Radanovic M. , Khoshelham K. , Fraser C. S. , Acharya D.
Published: December 2023

Abstract. Several methods exist that can be used to perform initial alignment of Building information models (BIMs) to the real building for Mixed Reality (MR) applications, such as marker-based or markerless visual methods, but this alignment is susceptible to drift over time. The existing model-based methods that can be used to maintain this alignment have multiple limitations, such as the use of iterative processes and poor performance in environments with either too many or not enough lines. To address these issues, we propose an end-to-end trainable Convolutional Neural Network (CNN) that takes a real and synthetic BIM image pair as input to regress the 6 DoF relative camera pose difference between them directly. By correcting the relative pose error we are able to considerably improve the alignment of the BIM to the real building. Furthermore, the results of our experiments demonstrate good performance in a challenging environment and high resilience to domain shift between synthetic and real images. A high localisation accuracy of approximately 7.0 cm and 0.9° is achieved which indicates the method can be used to reduce the camera tracking drift for MR applications.
MMCPP: A MULTI-MODAL CONTRASTIVE PRE-TRAINING MODEL FOR PLACE REPRESENTATION BASED ON THE SPATIO-TEMPORAL FRAMEWORK
Authors: Chen Y. , Yu X. S. , Qin K.
Published: December 2023

Abstract. The concept of "place" is crucial for understanding geographical environments from a human perspective. Place representation learning involves converting places into numerical low-dimensional dense vectors and is a fundamental procedure for artificial intelligence in geography (GeoAI). However, most studies ignore multi-level distance constraints and spatial proximity interactions that enable behavioral interactions between places. Furthermore, representing the temporal characteristics of these interactions in trajectory sequences poses a challenge for natural language processing and other field techniques. In addition, most existing methods rely on all modalities from inputs as they use joint training to integrate multiple modalities. To address these issues, we propose a Multi-Modal Contrastive Pre-training model for Place representation (MMCPP). Our model consists of three encoders that capture corresponding place attributes across different modalities, including point of interests (POIs), images, and trajectories. The trajectory encoder, named RodtFormer, takes fine-grained spatio-temporal trajectories as input and leverages self-attention with rotary temporal interval position embedding to simulate dynamic spatial and behavioral proximity interactions between places. By using a coordinated pre-training framework, MMCPP independently encodes place representations across different modalities and improves model reusability. We verify the effectiveness of our model on a taxi trajectory dataset using the location prediction task at next n seconds, including 30 seconds(s), 180(s), 300(s). Our results demonstrate that compared to existing embedding methods, our model is capable of learning higher-quality position representations during pre-training, leading to improved performance on downstream tasks.
APPLICATION OF CELL PHONE STATISTICS FOR ESTIMATING STRANDED PEOPLE BEHAVIOR AFTER SEVERE EARTHQUAKE IN TOKYO
Authors: Osaragi T.
Published: December 2023

Abstract. In this paper, we present a walking home simulation as anticipated after a large earthquake, and analyze people’s behaviors, walking and stopping, including the crowding of facilities by those unable to walk all the way home. For creating the necessary data for this simulation, we construct a method to estimate the spatiotemporal distribution of people with detailed individual information such as sex-age classification, and home location, by assembling population distribution data (Mobile Spatial Statistics and Person Trip survey data). The walking home simulation results verified significant variations in the crowding of facilities for stranded people due to differences in the day of the week and the time of the earthquake. Locations in Tokyo with insufficient numbers of facilities for stranded people were identified and some spatiotemporal characteristics of crowding, such as changes in crowding with time elapsed since the earthquake, were described.
MULTISPECTRAL IMAGE RESTORATION USING A VECTOR-VALUED REACTION-DIFFUSION BASED MIXED NOISE REMOVAL TECHNIQUE
Authors: Barbu T.
Published: December 2023

Abstract. A novel multispectral image filtering technique is proposed in this article. Since the multispectral images are often corrupted by mixed Poisson-Gaussian noise during the sensing and acquisition process, a nonlinear anisotropic diffusion-based restoration approach that deals efficiently with this type of noise mixture is considered here. A second-order vector-valued reaction-diffusion model that leads to a system of well-posed single-valued anisotropic diffusion equations coupled by correlation terms is introduced for this purpose. A finite difference method-based fast-converging approximation algorithm that solves numerically this nonlinear diffusion-based system is then proposed. This iterative numerical approximation scheme is successfully used for removing both the additive Gaussian and quantum noises while preserving the essential features of the multi-valued image. The effectiveness of the described mixed denoising technique is illustrated by the results of the restoration experiments and method comparisons that are also presented here. The proposed restoration approach enhances considerably the spectral image quality, making it well-prepared for the further MSI analysis and computer vision processes, such as the geospatial and remote sensing image analysis.
MACHINE LEARNING APPLICATION FOR CARBON ESTIMATION – A CASE STUDY
Authors: Jayanthi P. , Muralikrishna I.
Published: December 2023

Abstract. Climate change is a most global challenging issue. In this regard, a study on carbon dioxide, one of the pollutants causing the climate change is demonstrated in two different states of India viz., Visakhapatnam district (AP) and Shastri Nagar (RJ) for the period of April 2022 to January 2023. Carbon dioxide (CO2) is experiential on hourly and monthly basis for different seasons – summer (April), rainy (July) and winter (December). Most of air pollutants include NO2, CO2, PM2.5, PM10 etc. that are the major cause for climate change. The air quality in these zones is very poor, highly polluted and risk to humans. The study proved that CO2 is found comparatively low in rainy season over other. The machine learning regression models were modelled for Visakhapatnam and best models obtained are 1. Step-wise Linear Regression model with MSE (4.51E-28), RMSE (2.12E-14) and R-Squared (1) identified for rainy month. 2. Neural Network Narrow model with MSE (0.462), RMSE (0.680) and R-Squared (0.999) for winter month. 3. Linear Regression model with MSE (0.108), RMSE (0.329) and R-Squared (0.999) for summer. Similarly, the best models for Shastri Nagar for monthly data are 1. Step-wise Linear Regression with RMSE (20.292), MSE (411.774) and MAE (12.524) for April & May (summer), 2. Neural Network Narrow model had RMSE (3.399), MSE (11.554) and MAE (2.141) for July (rainy). 3. Neural Network Bilayered model with RMSE (1.618), MSE (2.619) and MAE (0.593) for November & December (winter). The results obtained were very efficient and reliable.
VEHICLE CLASSIFICATION IN URBAN REGIONS OF THE GLOBAL SOUTH FROM AERIAL IMAGERY
Authors: Mühlhaus M. , Kurz F. , Guridi Tartas A. R. , Bahmanyar R. , Azimi S. M. , Hellekes J.
Published: December 2023

Abstract. Land transport is a major contributor to the human-caused climate change; knowing the total number and composition of the vehicle fleet is key for estimating its emissions. Especially for countries of the Global South, emission inventories are associated with high uncertainties because fleet data are often unknown or outdated – classifying vehicles on remote sensing has the potential to change this. We present the XWHEEL dataset based on annotated vehicles in aerial images with six classes depending on the number of wheels, size and motorization. The dataset consists of 73 annotated aerial images of the city of Dar es Salaam (Tanzania) with 15,973 vehicles. To analyze the performance of the dataset, a convolutional neural network, ReDet, and a transformer-based neural network, DINOOBB, are trained with different configurations and validated on the validation and test split, but also on aerial images from other regions. The transformer-based DINO architecture has been adapted to the remote sensing domain and modified to predict Oriented Bounding Boxes. Results show a good performance on the test split from Dar es Salaam, when the two-wheeled classes are merged and the non-motorized three-wheeled vehicles are excluded due to their rare occurrence. The best performing algorithm configurations with four classes were then tested on aerial images of Kathmandu (Nepal) and Kampala (Uganda). The performance drops for cycles and three-wheeled vehicles, as their appearance varies between countries. A main finding is that we can reliably detect the different vehicle classes in Dar es Salaam. When algorithms trained on XWHEEL are generalized to other regions of the Global South, performance decreases for the more difficult classes (bicycles and tricycles). To obtain results that are comparable across the board, we therefore recommend expanding the dataset with additional annotations from other regions of the Global South.
VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VEHICLES USING SEGMENTATION-INITIALISED TRACKERS
Authors: Tilon S. M. , Nex F.
Published: December 2023

Abstract. We propose an effective vehicle tracker and speed estimation method from Unmanned Aerial Vehicles (UAVs) videos that can be deployed on UAV-embedded edge devices. Our tracker uses segmentation-derived vehicle regions to initialise a MOSSE tracker. This enables road operators to make multipurpose use of segmentation outputs while still being able to track the vehicles across frames. The vehicle speed is estimated using flight parameters derived from the UAV's flight computer and the vehicle displacement across frames. We trained CABiNet on the UAVid urban segmentation benchmark dataset and finetuned it on a dataset collected at our study site. A mean Intersection over Union (mIoU) of 0.73 was obtained for the vehicle class. Our segmentation-initialised MOSSE tracker was evaluated on the VisDrone Multi-Object Tracking (MOT) benchmark dataset and compared against traditional methods that utilise object regions for tracker initialisation. Our approach yielded a Multi-Object Tracking Precision (MOTP) of 0.872 compared to 0.830 when using YOLOv4. Our vehicle speed estimations approach was evaluated using a privately collected ground truth vehicle speed dataset. Our approach yielded a Root Mean Square Error (RMSE) between 3.42 and 16.12 km/hr across different flight configurations. Finally, our approach was deployed on an NVIDIA Jetson Xavier NX edge device and could be executed at 8 Frames Per Second (FPS). The results indicate that our approach is a simple yet fast alternative to traditional tracking methods while producing multipurpose segmentation information.
MONO-HYDRA: REAL-TIME 3D SCENE GRAPH CONSTRUCTION FROM MONOCULAR CAMERA INPUT WITH IMU
Authors: Udugama U. V. B. L. , Vosselman G. , Nex F.
Published: December 2023

Abstract. The ability of robots to autonomously navigate through 3D environments depends on their comprehension of spatial concepts, ranging from low-level geometry to high-level semantics, such as objects, places, and buildings. To enable such comprehension, 3D scene graphs have emerged as a robust tool for representing the environment as a layered graph of concepts and their relationships. However, building these representations using monocular vision systems in real-time remains a difficult task that has not been explored in depth.This paper puts forth a real-time spatial perception system Mono-Hydra, combining a monocular camera and an IMU sensor setup, focusing on indoor scenarios. However, the proposed approach is adaptable to outdoor applications, offering flexibility in its potential uses. The system employs a suite of deep learning algorithms to derive depth and semantics. It uses a robocentric visual-inertial odometry (VIO) algorithm based on square-root information, thereby ensuring consistent visual odometry with an IMU and a monocular camera. This system achieves sub-20 cm error in real-time processing at 15 fps, enabling real-time 3D scene graph construction using a laptop GPU (NVIDIA 3080). This enhances decision-making efficiency and effectiveness in simple camera setups, augmenting robotic system agility. We make Mono-Hydra publicly available at: https://github.com/UAV-Centre-ITC/Mono_Hydra.
REVERSE DOMAIN ADAPTATION FOR INDOOR CAMERA POSE REGRESSION
Authors: Acharya D. , Khoshelham K.
Published: December 2023

Abstract. Synthetic images have been used to mitigate the scarcity of annotated data for training deep learning approaches, followed by domain adaptation that reduces the gap between synthetic and real images. One such approach is using Generative Adversarial Networks (GANs) such as CycleGAN to bridge the domain gap where the synthetic images are translated into real-looking synthetic images that are used to train the deep learning models. In this article, we explore the less intuitive alternate strategy for domain adaption in the reverse direction; i.e., real-to-synthetic adaptation. We train the deep learning models with synthetic data directly, and then during inference we apply domain adaptation to convert the real images to synthetic-looking real images using CycleGAN. This strategy reduces the amount of data conversion required during the training, can potentially generate artefact-free images compared to the harder synthetic-to-real case, and can improve the performance of deep learning models. We demonstrate the success of this strategy in indoor localisation by experimenting with camera pose regression. The experimental results indicate an improvement in localisation accuracy is observed with the proposed domain adaptation as compared to the synthetic-to-real adaptation.
NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING
Authors: Anjanappa G. , Nikoohemat S. , Oude Elberink S. , Vôute R. L. , Lehtola V. V.
Published: December 2023

Abstract. Asset management systems are beneficial for maintaining building infrastructure and can be used to keep up-to-date records of relevant safety assets, such as smoke detectors, exit signs, and fire extinguishers. Existing methods for locating and identifying these assets in buildings primarily rely on surveys and images, which only provide 2D locations and can be tedious for large-scale structures. Indoor point clouds, which can be captured quickly for buildings using mobile scanning techniques, can provide us with 3D asset locations. In this paper, we study the feasibility of using 3D point clouds of buildings combined with deep learning techniques to identify safety-related assets, particularly small-sized assets like fire switches and exit signs. We adopt the state-of-the-art Deep Learning network, Kernel Point-Fully Convolutional Network (KP-FCNN), to identify these assets through semantic segmentation. Using the obtained results, we create a 3D-geometry model of the building with assets pinpointed, providing scene semantics and delivering more value. Our method is tested using three different point cloud datasets obtained from a depth camera, a mobile laser scanner, and an iPhone lidar sensor.
TEXTURE-BASED SEPARATION TO REFINE BUILDING MESHES
Authors: Vermandere J. , Bassier M. , Vergauwen M.
Published: December 2023

Abstract. In this paper, we present a texture-based separation approach to refine building meshes, which aims to address the challenges of detecting and isolating different objects in an indoor scene mesh. We propose a novel segmentation model based on the materials of the different parts of the scene. The proposed approach uses factorization-based texture segmentation to separate the different materials in the meshes and detect the edges on the segmented texture. To prevent large faces containing multiple materials from being segmented wrongly, the mesh is cut along the texture boundaries and new faces with a single material are created. Finally, we segment the new faces based on the material index and neighbouring faces using a region-growing algorithm. We evaluate the proposed approach on the Matterport indoor dataset, which shows that our approach performs well on detecting boundaries between distinct materials, but over-segments on complex shapes. Our proposed method improves the segmentation of large flat surfaces like posters or rugs.
AUTOMATIC GENERATION OF ROUTING GRAPHS FOR INDOOR-OUTDOOR TRANSITIONAL SPACE TO SUPPORT SEAMLESS NAVIGATION
Authors: Wang Z. , Zlatanova S. , Mostafavi M. A. , Khoshelham K. , Díaz-Vilariño L. , Li K.-J.
Published: December 2023

Abstract. With the fast development of urbanization, the complexity of built environments has dramatically increased, driving a need for assistance in seamless indoor-outdoor navigation. This requires integration of spatial information of indoor and outdoor environments from heterogeneous data sources. While outdoor road network data is largely available from many sources (such as OpenStreetMap), indoor spatial information is either inexistent or is inconsistently represented using several different standards. Among these standards, IndoorGML is a well-developed standard with the focus on indoor location-based services. This standard has already been accepted by Open Geospatial Consortium (OGC) and is now under active development. Although in IndoorGML some mechanisms have been defined to enable integration of indoor and outdoor networks, there is still a lack of concrete guidelines for determination of indoor-outdoor connections. It also lacks solid scientific foundations and efficient tools to extract the connecting nodes and edges that link indoor and outdoor spaces. To address this gap, in this study we focus on the connection of indoor and outdoor spaces and aim to provide a tool, which can automatically construct navigation graphs of the indoor-outdoor transitional space to support seamless integration of indoor-outdoor navigation. To this end, voxel-based modeling approaches are used to model the connecting space between indoor and outdoor environments. Based on Python, we develop the intended tool, which can generate voxel models from point clouds, identify navigable space by taking into account the characteristics of agents (such as pedestrians, wheelchairs, and vehicles), and automatically build navigation graphs linking IndoorGML networks with outdoor street networks. It is expected that the methodology and tools developed from this project will benefit the IndoorGML ecosystem and greatly advance the capability of IndoorGML in representing navigable space to support location-based services.
INDIVIDUAL TREE AGB ESTIMATION BASED ON FRACTAL PARAMETERS AND TREE VOLUME
Authors: Hui Z. , Xiong Y. , Xia Y. , Hui T. , Cheng P. , Cai Z.
Published: December 2023

Abstract. Forest is an important component of ecosystem. To estimate forest above-ground biomass (AGB) accurately, this paper proposed an individual tree AGB estimation method based on fractal geometry and individual tree volume. In this study, fractal parameters, such as fractal dimension and intercept were first calculated. And then, a fast tree volume estimation method based on point clouds voxelization was proposed. By combining fractal parameters, tree volume and specific wood density together, an individual tree AGB estimation method was developed. The datasets of three different tree species with harvest referenced AGB values were used for evaluating the performance of the developed model. Experimental results showed that the coefficient of determination (R2) of the developed model was 0.853. Compared with other four traditional allometric models, the proposed model performs the best no matter which accuracy indicator was adopted.
AUTOMATIC COARSE CO-REGISTRATION OF POINT CLOUDS FROM DIVERSE SCAN GEOMETRIES: A TEST OF DETECTORS AND DESCRIPTORS
Authors: Pirotti F. , Guarnieri A. , Chiodini S. , Bettanini C.
Published: December 2023

Abstract. Point clouds are collected nowadays from a plethora of sensors, some having higher accuracies and higher costs, some having lower accuracies but also lower costs. Not only there is a large choice for different sensors, but also these can be transported by different platforms, which can provide different scan geometries. In this work we test the extraction of four different keypoint detectors and three feature descriptors. We benchmark performance in terms of calculation time and we assess their performance in terms of accuracy in their ability in coarse automatic co-registration of two clouds that are collected with different sensors, platforms and scan geometries. One, which we define as having the higher accuracy, and thus will be used as reference, was surveyed via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox Horizon over a walking path with un-even ground.The novelty in this work consists in comparing several strategies for fast alignment of point clouds from very different surveying geometries, as the drone has a bird’s eye view and the bicycle a ground-based view. An added challenge is related to the lower cost of the bicycle sensor ensemble that, together with the rough terrain, reasonably results in lower accuracy of the survey. The main idea is to use range images to capture a simplified version of the geometry of the surveyed area and then find the best features to match keypoints. Results show that NARF features detected more keypoints and resulted in a faster co-registration procedure in this scenario whereas the accuracy of the co-registration is similar to all the combinations of keypoint detectors and features.
LIDAR-INERTIAL LOCALIZATION WITH GROUND CONSTRAINT IN A POINT CLOUD MAP
Authors: Ai M. , Asl Sabbaghian Hokmabadi I. , Elhabiby M. , Moussa M. , Zekry A. , Mohamed A. , El-Sheimy N.
Published: December 2023

Abstract. Real-time localization is a crucial task in various applications, such as automatic vehicles (AV), robotics, and smart city. This study proposes a framework for map-aided LiDAR-inertial localization, with the objective of accurately estimating the trajectory in a point clouds map. The proposed framework addresses the localization problem through a factor graph optimization (FGO), enabling the fusion of homogenous measurements for sensor fusion and designed absolute and relative constraints. Specifically, the framework estimates the light detection and ranging (LiDAR) odometry by leveraging inertial measurement unit (IMU) and registering corresponding featured points. To eliminate the accumulative error, this paper employs a ground plane distance and a map matching error to constraint the positioning error along the trajectory. Finally, local odometry and constraints are integrated using a FGO, including LiDAR odometry, IMU pre-integration, and ground constraints, map matching constraints, and loop closure. Experimental results were evaluated on an open-source dataset, UrbanNav, with an overall localization accuracy of 2.29 m (root mean square error, RMSE).
HYBRID DEEP LEARNING APPROACH FOR VEHICLE’S RELATIVE ATTITUDE ESTIMATION USING MONOCULAR CAMERA
Authors: Haggag M. , Moussa A. , El-Sheimy N.
Published: December 2023

Abstract. Relative pose estimation using a monocular camera is one of the most common approaches for aiding vehicle’s navigation. It involves determining the position and orientation of a vehicle relative to its surroundings using only a single camera. This can be achieved through four main steps: feature detection and matching, motion estimation, filtering and optimization, and scale estimation. Feature tracking involves detecting and tracking distinctive visual features in the environment, such as corners or edges, and using their relative motion to estimate the camera's movement. This approach can be prone to errors due to feature detection and tracking difficulties, as well as issues with moving objects, occlusions, and changes in lighting conditions. These typical computer vision approaches are computationally intensive and may require significant processing power as well, which limits their real time application. This paper proposes a hybrid deep neural network approach for estimating the relative attitude of a vehicle using a monocular camera to aid in vehicle navigation. The proposed neural network adopts a relatively shallow architecture to minimize the computational cost and to meet the real-time requirements of low-cost processing systems. The network is trained using the KITTI dataset and can estimate the relative attitude of the vehicle with a RMSE of relative orientation of 0.017 degrees per frame. The processing time of the proposed approach is around 28 ms per frame including both the tracking and network prediction steps, which is significantly faster than the typical estimation pipelines. The results show that the proposed approach is a viable alternative to conventional computer vision methods and can significantly reduce computational costs, deal with the confusing scenarios of the moving objects while maintaining a good accuracy in estimating ego-motion.
HIGH-RESOLUTION MAPPING OF FORESTED HILLS USING REAL-TIME UAV TERRAIN FOLLOWING
Authors: Singh C. H. , Mishra V. , Jain K.
Published: December 2023

Abstract. This study presents a novel method for high-altitude terrain following photogrammetric mapping using Unmanned Aerial Vehicles (UAVs) that adapt their height to the terrain's morphology. Traditional grid missions in UAV mapping surveys use nadir photographs to cover an area of interest which is taken from a fixed height from the datum.Many times this leads to difficulties in flight mission and processing when hilly/undulated terrain is under consideration. The proposed solution addresses this by adjusting the UAV's flying height based on real-time terrain information gathered by a range detection sensor. This paper tests the proposed method in a hilly area in the Himalayas, capturing high-resolution images and processing them using UAV-based photogrammetry software. The pre-acquired terrain information ensures safe separation from obstacles and enables trajectory generation at lower altitudes when planning initial flights. The experimental results validate the effectiveness of the proposed method and highlight its potential for generating high-resolution photogrammetric products applicable to various UAV mapping tasks and monitoring scenarios. Overall, this research contributes to advancing the field of UAV mapping by providing a practical and effective solution for UAV based surveys in hilly and undulated terrains. The findings of this study have significant implications for various applications, including land surveying, environmental monitoring, landslide mapping and other scenarios that require detailed and accurate data acquisition from UAVs.
MONITORING LAND SUBSIDENCE IN EGYPT’S NORTHERN WEST COAST USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR
Authors: ElGharbawi T.
Published: December 2023

Abstract. The northern west coast of Egypt has experienced significant development and urbanization in response to its growing population. This has resulted in the construction of many new cities along the Mediterranean Sea shore, making the region a major attraction for both economic and recreational purposes. However, this rapid growth has also created a need for continuous monitoring to ensure environmental and economic sustainability and to identify potential hazards. Remote sensing techniques, such as satellite imagery and interferometric synthetic aperture radar (InSAR), have proven to be effective tools for monitoring remote regions. However, InSAR has some limitations, such as susceptibility to tropospheric and ionospheric delays as well as baseline errors. To address these limitations, this study utilized a differential technique that uses the phase difference between two nearby pixels to reduce the spatially correlated atmospheric and baseline errors. In this study, Sentinel-1 SAR images were used to create a single master InSAR stack covering the study region from 2018 to 2023. The estimated land surface velocities showed a subsidence motion of 10 to 15 mm/year in the region of Marsa Matruh city, which is consistent with existing urbanization and agricultural activities. The findings of this study highlight the importance of continuous monitoring and the potential of remote sensing techniques for identifying potential hazards and ensuring sustainable development.
SHIP DETECTION IN COSMO-SKYMED SAR IMAGERY USING A NOVEL CNN-BASED DETECTOR: A CASE STUDY FROM THE SUEZ CANAL
Authors: Saleh T. , Holail S. , Weng X. , Xiao X. , Xia G.-S.
Published: December 2023

Abstract. The Suez Canal, strategically located as the shortest international sea route, plays a crucial role in facilitating the transportation of goods between Asia and Europe. However, the occurrence of traffic disruptions within the Canal poses a serious threat to global trade, as evidenced by the recent incident of the container ship Ever Given, which ran aground in the Suez Canal on March 23, 2021. This event led to a complete blockade of the Canal that lasted for six days, resulting in a fleet of ships waiting to pass through the Canal. This highlights the need to monitor the Canal to prevent similar disturbances in the future. In this paper, we propose a CNN-based attention-guided self-learning framework for ship detection from 3m high-resolution COSMO-SkyMed SAR imagery acquired in April 2021 via the Egyptian Suez Canal. We introduce a self-learning augmented segmentation (SLAS) technique to augment the dataset with new ship samples by pseudo-labeling an unlabeled dataset. We also present the Attention-guided Feature Refinement (AFR) module to extract more significant semantic features and contextual information, especially for ships of varying sizes in SAR images. Finally, the AFR module is fed into a Region Proposal Network (RPN) to generate a set of proposal anchors, which are later used in a Deep Detection Network (DDN) for ship classification and localization. Our experimental results demonstrate that the proposed method outperforms current state-of-the-art detection models in terms of detection accuracy, particularly in complex coastal scenes, with an overall accuracy of up to 87% mean average precision (mAP).
PERFORMANCE ASSESSMENT OF OBJECT DETECTION FROM MULTI SATELLITES AND AERIAL IMAGES
Authors: Ahmed M. , El-Sheimy N. , Leung H. , Kamel A. M. , Moussa A.
Published: December 2023

Abstract. Object detection in remote sensing imagery plays an important role in many applications, such as tracking and change detection. With the development of deep learning algorithms and advancement in hardware systems, improved accuracies have been achieved in the detection of various objects from remote sensing images. However, object detection across heterogeneous remote sensing imagery remains an important issue, particularly for satellite and aerial imagery. The colour variation for the same ground objects, variable resolutions, different platform heights, the parallax effect, and image distortion brought on by diverse shooting angles are the biggest hurdles in satellite-aerial detection applications. The research aims to obtain successful model for detecting aircrafts from satellite and aerial images and reduce cost and the gap of revisit time between sensors. The networks were tested using aerial, GF-2, Jilin-1 (JL-1) and Pleiades satellites test sets after being trained individually using the RGB high-resolution aerial set and panchromatic low-resolution GF-2 satellite set to validate the efficiency of the trained models. Also, the aerial-trained model and GF-2 satellite-trained model as dedicated models were compared with each other, and model trained by all dataset for Object Detection in Aerial Images (DOTA). It is observed that the anchor sizes and augmentation methods can enhance the performance of detection models. k-means algorithm and data augmentation were applied to produce better anchor box selection and avoid overfitting, atmospheric conditions problems, respectively. The accuracy assessment results demonstrate that the aerial-trained model outperforms the GF-2 satellite-trained model. In addition, the results of two dedicated detection models show improved accuracy compared to the DOTA-trained model.
A NEW WEATHER FILTER FOR REDUCING WEATHER EFFECT IN CALCULATING SEA ICE CONCENTRATION FROM AMSR2 DATA
Authors: Cho K. , Naoki K.
Published: December 2023

Abstract. Ice concentration is one of the most fundamental parameters of sea ice which can be calculated from brightness temperatures measured by passive microwave radiometers such as AMSR2 onboard satellites. However, the presence of atmospheric water vapor, cloud liquid water, etc. is likely to increase the brightness temperatures over open water. As a result, sometimes, certain ice concentrations are calculated over open water covered with heavy clouds. The authors call this kind of area as “false sea ice” and the phenomenon is called the weather effect. In the AMSR2 ice concentration product, a weather filter is used to reduce the weather effect. However, it is known that false sea ice appears occasionally in some areas even in the summertime in AMSR2 ice concentration products. In this study, the authors have modified the original weather filter and added a new weather filter utilizing the following two equations which are TB(23GHzV)<−0.75×(TB(36GHzV)−TB(36GHzH))+253K and TB(36GHzV)−TB(36GHzH) < 57K. If the brightness temperature of the pixel meets the above two equations, we set the ice concentration of the pixel as 0%. By adding the new weather filter, most of the false sea ice that appeared in the ASMR2 ice concentration products could be rejected. JAXA has decided to add this weather filter for producing AMSR2 standard sea ice concentration products.
ESTIMATION OF WHEAT KERNEL MOISTURE CONTENT IN-FIELD BASED ON PLANETSCOPE AND SENTINEL-2 SATELLITE IMAGES
Authors: Luo J. , Wu Z. , Rao K. , Lin H. , Zhang S. , Dai Z. , Lin W. , Yue Y.
Published: December 2023

Abstract. Strict limitation of wheat kernel moisture content (KMC) has been set during wheat trading, as it determines the quality, storage safety, and economic efficiency. The acquisition of timely and precise wheat KMC data in-field constitutes a vital component of harvest management, as it can enable farmers to gather grain that meets industry standards, optimize their financial returns, and safeguard food resources. However, so far efficient monitoring methods have remained elusive. To address this challenge, this study utilized remote sensing satellite imagery, specifically, PlanetScope (PS) and Sentinel-2 (S2), to bridge this crucial gap. By leveraging the sensitive bands and vegetation indices for wheat KMC that were extracted from S2 and PS, respectively, this study constructed wheat KMC estimation models utilizing Random Forest Regression (RFR) to achieve high accuracy (R2>0.85). Furthermore, this study evaluated different spectral feature combinations to optimize the mapping retrieval quality of wheat KMC monitoring. Notably, the results revealed that the B5 band on PS was the most effective original band for wheat KMC monitoring, while B11 and B12 on S2 performed well but were susceptible to soil background interference along field edges. In terms of vegetation indices, the Plant Senescence Reflectance Index (PSRI) was deemed a reliable monitoring indicator. The practical implications of this study provided a dependable and convenient tool for monitoring wheat KMC in-field and scientific methods to assist harvest decision-making.