Faculty Publications

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Publications by NITK Faculty

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Subject: search.filters.subject.Digital elevation model

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Now showing 1 - 6 of 6
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    Integrated coastal zone management plan and coastal zone information system for Mangalore Coast, West Coast of India
    (2006) Dwarakish, G.S.; Shetty, D.; Rajarama; Pai, J.; Natesan, U.
    In the present study, Integrated Coastal Zone Management Plan (ICZMP) has been developed for Mangalore Coast in Karnataka, along the West Coast of India, by analyzing the remotely sensed data and conventional data. The various data products used in the present study includes, IRS-1C LISS-III+PAN and IRS-P6 LISS IV remotely sensed data, Naval Hydrographic Chart and Survey of India (SOI) toposheets. Different thematic maps prepared in the present study includes, land use/ land cover map, bathymetry map, shoreline configuration map, transportation and drainage network maps, GPS survey map, CRZ map, contour map, DEM, inundation map and coastal erosion vulnerability map. The results of the present study are encouraging. Some of the specific conclusions of the study are; eight coastal vulnerability sites have been identified, significant increase in the built-up area and decrease in the agricultural land, no large scale erosion or deposition in the vicinity of coastal structures such as seawalls, breakwaters and entrance channel of New Mangalore Port Trust and the beaches along the Mangalore Coast are maintaining dynamic equilibrium. To get the online information about all these, Coastal Zone Information System (CZIS) has been developed through V. B. 6. 0. using results of various data analyses.
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    Integrated coastal zone management plan for udupi coast using RS, GIS and GPS
    (2007) Dwarakish, G.S.; Vinay, S.A.; Dinakar, S.M.; Pai, J.; Mahaganesha, K.; Natesan, U.
    Coastal areas are under great pressure due to increase in human population and industrialization/commercialization and hence these areas are vulnerable to environmental degradation, resource reduction and user conflicts. In the present study an Integrated Coastal Zone Management Plan (ICZMP) has been developed for Udupi Coast in Karnataka, along West Coast of India. The various data products used in the present study includes IRS-IC LISS-III + PAN and IRS-P6 LISS III remotely sensed data, Naval Hydrographic Charts and Survey of India (SOI) toposheets, in addition to ground truth data. Thematic maps such as land use/ land cover map, bathymetry map, shoreline configuration map, transportation and drainage network maps, GPS survey map, CRZ map, contour map, DEM, inundation map, critical erosion area map were prepared. A Coastal Vulnerability Index has also been calculated for the study area to know the resistance of study area to sea level rise and is demarcated into four categories; Very high, High, Moderate and Low vulnerability, and a vulnerability map has been prepared. The results of the present study are encouraging. Some of the specific conclusions of the study are; about 50% study area is prone to erosion, river mouths along study area show shifting tendency towards south, and the beaches along the Udupi Coast are maintaining dynamic equilibrium. Coastal Zone Information System (CZIS) has been developed through V.B.6.0 using results of various data analysis.
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    Integrated coastal zone management plan for Udupi coast using remote sensing, geographical information system and global position system
    (SPIE spie@spie.org, 2008) Dwarakish, G.S.; Vinay, S.A.; Dinakar, S.M.; Pai, B.J.; Mahaganesha, K.; Natesan, U.
    Coastal areas are under great pressure due to increase in human population and industrialization/commercialization and hence these areas are vulnerable to environmental degradation, resource reduction and user conflicts. In the present study an Integrated Coastal Zone Management Plan (ICZMP) has been developed for Udupi Coast in Karnataka, along West Coast of India. The various data products used in the present study includes IRS-1C LISS-III + PAN and IRS-P6 LISS III remotely sensed data, Naval Hydrographic Charts and Survey of India (SOI) toposheets, in addition to ground truth data. Thematic maps such as land use/ land cover map, bathymetry map, shoreline configuration map, transportation and drainage network maps, GPS survey map, CRZ map, contour map, DEM, inundation map, critical erosion area map were prepared. A Coastal Vulnerability Index has also been calculated for the study area to know the resistance of study area to sea level rise and is demarcated into four categories; Very high, High, Moderate and Low vulnerability, and a vulnerability map has been prepared. The results of the present study are encouraging. Some of the specific conclusions of the study are; about 50% study area is prone to erosion, river mouths along study area show shifting tendency towards south, and the beaches along the Udupi Coast are maintaining dynamic equilibrium. Coastal Zone Information System (CZIS) has been developed through V.B.6.0 using results of various data analysis. © 2008 Society of Photo-Optical Instrumentation Engineers.
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    Demonstration of structure-from-motion (SfM) and multi-view stereo (MVS) close range photogrammetry technique for scour hole analysis
    (Springer, 2021) Mali, V.K.; Venu, P.; Nagaraj, M.K.; Kuiry, S.N.
    Comprehensive data collection remains a challenge in the field of sediment research. The manual acquisition of fine-gridded data is almost infeasible even for a laboratory setup. Therefore, this paper demonstrates a simple and cost-effective SfM–MVS technique to acquire accurate morphological data. This data further can be used for assessing the scour development around a bridge pier. For this purpose, the experiments are conducted for clear-water scour around circular and hexagonal piers for three different discharges. Before the start of the experimental run, a set of overlapped images is taken using the digital camera. Once the experiment run is completed, the water in the flume is completely drained off and then again another set of overlapped photos are taken. A total of eight ground control points (GCPs) is used to transform the generated relative three-dimensional cloud points to the absolute local coordinate system. Eventually, the high-spatial resolution digital elevation models (DEMs) are generated using the SfM–MVS photogrammetry technique. A statistical analysis is performed between the checkpoints (observed data) and DEM predicted points, which revealed that the generated DEMs show high accuracy in all the cases. It is therefore concluded that the SfM–MVS technique can be applied to understand the morghological changes around any shape of the piers. Thus, the proposed image analysis method can be adopted for obtaining the high spatial resolution data for sediment transport research. © 2021, Indian Academy of Sciences.
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    Performance Prediction Model Development for Solar Box Cooker Using Computational and Machine Learning Techniques
    (American Society of Mechanical Engineers (ASME), 2023) Anilkumar, B.C.; Maniyeri, R.; Anish, S.
    The development of prediction models for solar thermal systems has been a research interest for many years. The present study focuses on developing a prediction model for solar box cookers (SBCs) through computational and machine learning (ML) approaches. The prime objective is to forecast cooking load temperatures of SBC through ML techniques such as random forest (RF), k-nearest neighbor (k-NN), linear regression (LR), and decision tree (DT). ML is a commonly used form of artificial intelligence, and it continues to be popular and attractive as it finds new applications every day. A numerical model based on thermal balance is used to generate the dataset for the ML algorithm considering different locations across the world. Experiments on the SBC in Indian weather conditions are conducted from January through March 2022 to validate the numerical model. The temperatures for different components obtained through numerical modeling agree with experimental values with less than 7% maximum error. Although all the developed models can predict the temperature of cooking load, the RF model outperformed the other models. The root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and mean square error (MSE) for the RF model are 2.14 (°C), 0.992, 1.45 (°C), and 4.58 (°C), respectively. The regression coefficients indicate that the RF model can accurately predict the thermal parameters of SBCs with great precision. This study will inspire researchers to explore the possibilities of ML prediction models for solar thermal conversion applications. © © 2023 by ASME.
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    An Ensemble of Vision-Language Transformer-Based Captioning Model With Rotatory Positional Embeddings
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sathyanarayana, K.B.; Naik, D.
    Image captioning is a dynamic and crucial research area focused on automatically generating image textual descriptions. Traditional models, primarily employing an encoder-decoder framework with Convolutional Neural Networks (CNNs), often struggle to capture the complex spatial and sequential relationships inherent in visual data. This gap in performance underscores the necessity for more sophisticated solutions. The proposed work introduces a groundbreaking ensemble model that integrates CNN, Graph Convolutional Network (GCN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer architectures. Our approach achieves an outstanding 97% increase in CIDEr scores on the Flickr30K dataset and a remarkable 28.6% improvement on the Flickr8K dataset, thanks to the innovative implementation of Rotary Positional Encoding (RoPE). By strategically incorporating GCN and BiLSTM layers, our model adeptly captures essential relationships within the data. This groundbreaking research effectively addresses the challenges of image captioning, leveraging a powerful combination of advanced architectures. As a result, our model significantly enhances the generation of accurate and contextually rich captions, positioning it as a game-changer for automated image-to-text applications. The proposed Ensemble model with RoPE, achieved impressive performance on the Flickr8k and Flickr30k datasets, with scores of 80.62 and 95.0 for BLEU-1, 72.01 and 90.51 for BLEU-2, 63.12 and 81.24 for BLEU-3, 48.32 and 68.8 for BLEU-4, 74.26 and 81.89 for METEOR, 80.24 and 84.29 for ROUGE-L, 118.94 and 155.77 for CIDEr, and 48.7 and 39.0 for SPICE, respectively. © 2013 IEEE.