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Evaluating and Improving RoSELS for Road Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences 1

3rd International Conference on Deep Learning Theory and Applications, DeLTA 2022

作者： Katkoria, Dhvani Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab IIIT Bangalore 26/C Electronics City Karnataka 560100 India

ISBN: (数字)9783031373176

ISBN: (纸本)9783031373169

Navigable space determination is a difficult problem encountered in robotics and intelligent vehicle technology and it requires integrated solutions using advances in computer vision and computational geometry. The conventional data processing workflow uses semantic segmentation to identify road points from three-dimensional (3D) automotive LiDAR point clouds, which have to be extended to determine its boundary points. The boundary points are critical in getting an initial coarse extraction of its surface. Hence, to filter the curb or edge points from the road points, the semantic segmentation results are post-processed. The road surface is determined by the entire trajectory information, which is captured as a sequence of LiDAR point clouds. For the entire road surface extraction, we use an automated system, RoSELS (Road Surface Extraction for LiDAR point cloud Sequence) that works at different scales, i.e., at the scale of a point, a point cloud, and the sequence of point clouds. In this paper, we evaluate the algorithms used in RoSELS, namely, the Gaussian Mixture Model for curb detection and ResNet-50 for transfer learning in frame classification. We evaluate the quality of the mesh extracted using RoSELS, which is intended for straight-road geometry. Here, we also show how the initial extracted surface can be further post-processed to extend RoSELS for curved roads. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicles

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A Methodology for Integrating Population Health Surveys Using Spatial Statistics and visualizations for Cross-Sectional Analysis

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SN Computer Science 2023年第3期4卷 224页

作者： Ravindra, Harshitha Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab E-Health Research Center IIIT Bangalore 26/C Electronics City Hosur Road Karnataka Bangalore 560100 India

Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density comes at a high economic cost. To conduct surveys at low-cost and efficiently, multiple surveys with different, but focused, goals are implemented through various organizations in a decentralized manner. Some of the surveys tend to overlap in outcomes with spatial, temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under-five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating the outcomes of both surveys. Malnutrition in children under-five is a pertinent global public health problem that is widely prevalent in India. Our work shows that such an integrated analysis is beneficial alongside independent analyses of such existing national surveys to find new insights into national health indicators. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anemia Hellinger distance Large-scale surveys Micronutrient deficiency Multi-source data fusion Spatial analysis Spatial autocorrelation Stunting Underweight visualization Wasting

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BarChartAnalyzer: Data Extraction and Summarization of Bar Charts from Images

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SN Computer Science 2022年第6期3卷 500页

作者： Daggubati, Siri Chandana Sreevalsan-Nair, Jaya Dadhich, Komal Graphics-Visualization-Computing Lab IIIT Bangalore 26/C Electronics City Hosur Road Karnataka Bangalore 560100 India

Charts or scientific plots are widely used visualizations for efficient knowledge dissemination from datasets. However, these charts are predominantly available in image format. There are various scenarios where these images are interpreted in the absence of their source data table. This leads to a pertinent need for data extraction from an available chart image. We narrow down our scope to bar charts and its subtypes. We propose a semi-automated workflow, BarChartAnalyzer, for data extraction from chart images. Our workflow integrates the following tasks in sequence: chart type classification, image annotation, object detection, text detection and recognition, data table extraction, chart summarization, and, optionally, chart redesign. Our data extraction uses second-order tensor fields from tensor voting used in computer vision. Here, we propose a novel application of design study methodology for the chart summarization component. Our results show that our workflow can effectively and accurately extract data from images of different resolutions and subtypes of bar charts. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bar charts Chart classification Chart image analysis Chart segmentation Convolutional neural Network Data extraction Design study methodology Grouped bar charts Histograms Optical character recognition Stacked bar charts Text recognition Text summarization

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CCESAR: COASTLINE CLASSIFICATION-EXTRACTION FROM SAR IMAGES USING CNN-U-NET COMBINATION

arXiv

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arXiv 2025年

作者： Arora, Vidhu Gupta, Shreyan Kudupu, Ananthakrishna Priyadarshi, Aditya Mundayatt, Aswathi Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab International Institute of Information Technology Karnataka Bangalore560100 India

In this article, we improve the deep learning solution for coastline extraction from Synthetic Aperture Radar (SAR) images by proposing a two-stage model involving image classification followed by segmentation. We hypothesize that a single segmentation model usually used for coastline detection is insufficient to characterize different coastline types. We demonstrate that the need for a two-stage workflow prevails through different compression levels of these images. Our results from experiments using a combination of CNN and U-Net models on Sentinel-1 images show that the two-stage workflow, coastline classification-extraction from SAR images (CCESAR) outperforms a single U-Net segmentation model. © 2025, CC BY-SA.

关键词： Image segmentation

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EVOLUTION OF DATA-DRIVEN SINGLE- AND MULTI-HAZARD SUSCEPTIBILITY MAPPING AND EMERGENCE OF DEEP LEARNING METHODS

arXiv

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arXiv 2025年

作者： Sreevalsan-Nair, Jaya Mundayatt, Aswathi Graphics-Visualization-Computing Lab International Institute of Information Technology Karnataka Bangalore560100 India

Data-driven susceptibility mapping of natural hazards has harnessed the advances in classification methods used on heterogeneous sources represented as raster images. Susceptibility mapping is an important step towards risk assessment for any natural hazard. Increasingly, multiple hazards co-occur spatially, temporally, or both, which calls for an in-depth study on multi-hazard susceptibility mapping. In recent years, single-hazard susceptibility mapping algorithms have become well-established and have been extended to multi-hazard susceptibility mapping. Deep learning is also emerging as a promising method for single-hazard susceptibility mapping. Here, we discuss the evolution of methods for a single hazard, their extensions to multi-hazard maps as a late fusion of decisions, and the use of deep learning methods in susceptibility mapping. We finally propose a vision for adapting data fusion strategies in multimodal deep learning to multi-hazard susceptibility mapping. From the background study of susceptibility methods, we demonstrate that deep learning models are promising, untapped methods for multi-hazard susceptibility mapping. Data fusion strategies provide a larger space of deep learning models applicable to multi-hazard susceptibility mapping. © 2025, CC BY.

关键词： Mapping

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Scaling Up Study Area Size in Flood Susceptibility Mapping

Scaling Up Study Area Size in Flood Susceptibility Mapping

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Aswathi Mundayatt Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Flood susceptibility mapping (FSM) is crucial for preparedness and risk mitigation of floods, and machine learning (ML)-based FSM methods have been well-studied. However, due to computational constraints, geographical variations, and differential data availability, FSM is often confined to smaller regions, much less than 1,000 km 2 . To address the computational constraints of FSM generation in data-rich regions, we propose a data-parallel approach using PySpark with two levels of spatial partitioning, where they address the computational load and geographical variations, respectively. In our case study of the state of Kerala, India, we propose grid lattice partitioning and the Ecologically Sensitive Zone (ESZ) map for the two levels of spatial partitioning, respectively. The latter is used to train random forest classifier models. We investigate the influence of contiguity in training grids on FSM outcomes within different ESZs. Our quantitative and qualitative comparative analysis of observations demonstrate the importance of contiguous training grids, especially in capturing finer details in FSM. Our implementation demonstrates the efficiency of our approach in the study area of Kerala spanning 38,863 km 2 .

关键词： Training Accuracy Computational modeling Biological system modeling Scalability Lattices Computational efficiency

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IMGD: Image-based Multiscale Global Descriptors of Airborne LiDAR Point Clouds Used for Comparative Analysis 8

IMGD: Image-based Multiscale Global Descriptors of Airborne ...

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8th Smart Tools and Applications in graphics Conference, STAG 2021

作者： Sreevalsan-Nair, J. Mohapatra, P. Singh, S. Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore India

ISBN: (纸本)9783038681656

Both geometric and semantic information are required for a complete understanding of regions acquired as three-dimensional (3D) point clouds using the Light Detection and Ranging (LiDAR) technology. However, the global descriptors of such datasets that integrate both the information types are rare. With a focus on airborne LiDAR point clouds, we propose a novel global descriptor that transforms the point cloud from Cartesian to barycentric coordinate spaces. We use both the probabilistic geometric classification, aggregated from multiple scales, and the semantic classification to construct our descriptor using point rendering. Thus, we get an image-based multiscale global descriptor, IMGD. To demonstrate its usability, we propose the use of distribution distance measures between the descriptors for comparing the point clouds. Our experimental results demonstrate the effectiveness of our descriptor, when constructed of publicly available datasets, and on applying our selected distance measures. © 2021 The Author(s)

关键词： Uncertainty analysis

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Tensor Fields for Data Extraction from Chart Images: Bar Charts and Scatter Plots 8th

Tensor Fields for Data Extraction from Chart Images: Bar Cha...

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8th Workshop on Topological Methods in Data Analysis and visualization, TopoInVis 2019

作者： Sreevalsan-Nair, Jaya Dadhich, Komal Daggubati, Siri Chandana Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Karnataka India

ISBN: (纸本)9783030834999

Charts are an essential part of both graphicacy (graphical literacy), and statistical literacy. As chart understanding has become increasingly relevant in data science, automating chart analysis by processing raster images of the charts has become a significant problem. Automated chart reading involves data extraction and contextual understanding of the data from chart images. In this paper, we perform the first step of determining the computational model of chart images for data extraction for selected chart types, namely, bar charts, and scatter plots. We demonstrate the use of positive semidefinite second-order tensor fields as an effective model. We identify an appropriate tensor field as the model and propose a methodology for the use of its degenerate point extraction for data extraction from chart images. Our results show that tensor voting is effective for data extraction from bar charts and scatter plots, and histograms, as a special case of bar charts. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Composition of Geospatial visualizations for Scale-aware Views of Multiple Outcome Variables in Population Surveys

Composition of Geospatial Visualizations for Scale-aware Vie...

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International Conference on Information Visualisation (IV)

作者： Harshitha Ravindra Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab IIIT Bangalore Bangalore India

ISBN: (纸本)9781665490085

Population survey data is important for understanding the status of well-being along any dimensions, i.e., social, economic, political, health, etc. This data generates spatial point patterns which can be explored and analyzed using visualization. Given the spatial aspect of the data, there is a requirement of using cartographic maps, which are mostly limited to visualizing a single variable in most cases. Here, it is also important that the choice of visualizations also enable scale-aware analysis when zooming in and out of the maps, since the data is from the smaller political units and can be aggregated to larger political units. Thus, we explore the different visual compositions which use mathematical operators and the composite layouts for visualizing multiple outcome variables in survey data. The mathematical operators allow the use of univariate and bivariate data modeling and representation, and composite layouts of interest are juxtaposition and superimposed views. We demonstrate the inferences from visualizations using a case study on malnutrition in children under five in India. Our work shows that a visual composition of binary relationships represented in a visualization and a juxtaposed layout of such pairwise variables is effective in making inferences from the multivariate spatial point patterns in population data.

关键词： Heating systems visualization Sociology Layout Data visualization Spatial databases Mathematical models

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CAP-DSDN: Node Co-association Prediction in Communities in Dynamic Sparse Directed Networks and a Case Study of Migration Flow 14

CAP-DSDN: Node Co-association Prediction in Communities in D...

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14th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022

作者： Sreevalsan-Nair, Jaya Jakher, Astha Graphics-Visualization-Computing Lab. International Institute of Information Technology Bangalore Bangalore India Department of Humanities and Social Sciences IIT Kharagpur West Bengal Kharagpur721302 India

ISBN: (纸本)9789897586149

Predicting the community structure in the time series, or snapshots, of a real-world graph in the future, is a pertinent challenge. This is motivated by the study of migration flow networks. The dataset is characterized by edge sparsity due to the inconsistent availability of data. Thus, we generalize the problem to predicting community structure in a dynamic sparse directed network (DSDN). We introduce a novel application of co-association which is a pairwise relationship between the nodes belonging to the same community. We thus propose a three-step algorithm, CAP-DSDN, for co-association prediction (CAP) in such a network. Given the absence of benchmark data or ground truth, we use an ensemble of community detection (CD) algorithms and evaluation metrics widely used for directed networks. We then define a metric based on entropy rate as a threshold to filter the network for determining a significant and data-complete subnetwork. We propose the use of autoregressive models for predicting the co-association relationship given in its matrix format. We demonstrate the effectiveness of our proposed method in a case study of international refugee migration during 2000–18. Our results show that our method works effectively for migration flow networks for short-term prediction and when the data is complete across all snapshots. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Forecasting

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