Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014729
Anna C. Berlin, V. R. Chowdhary, N. Menon
Flooding is one of the most prevalent natural disasters in Assam, India, occurring on a yearly basis. In this paper, a Geographic Information System (GIS) approach is applied, with the help of remote sensing data, to create a flood risk map of the state of Assam. This map is based on five important parameters: population, land use/land cover, elevation, precipitation and distance to the nearest water body. Furthermore, Sentinel-1 data is used to create an inundation map of the most recent flood, which occurred during June of 2022. Both aspects of this research help to assess the situation on ground during a flood and to improve the flood management and preparedness for future flood scenarios.
{"title":"Flood Risk and Inundation Mapping of Assam using an Approach based on Geospatial Technology","authors":"Anna C. Berlin, V. R. Chowdhary, N. Menon","doi":"10.1109/PuneCon55413.2022.10014729","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014729","url":null,"abstract":"Flooding is one of the most prevalent natural disasters in Assam, India, occurring on a yearly basis. In this paper, a Geographic Information System (GIS) approach is applied, with the help of remote sensing data, to create a flood risk map of the state of Assam. This map is based on five important parameters: population, land use/land cover, elevation, precipitation and distance to the nearest water body. Furthermore, Sentinel-1 data is used to create an inundation map of the most recent flood, which occurred during June of 2022. Both aspects of this research help to assess the situation on ground during a flood and to improve the flood management and preparedness for future flood scenarios.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"21 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131164135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014983
S. Shilaskar, Shreyas Talwekar, S. Bhatlawande, Sumitsaurabh Singh, R. Jalnekar
Brain-Computer Interfaces (BCI) make use of Electroencephalogram (EEG) signals to classify limb movements and other motor activities in various biomedical applications. This paper presents an EEG-based system to distinguish span and hook hand gestures. The proposed model consists of various signal processing techniques to extract features of interest and machine learning-based classification algorithms. We have extracted features based on statistical parameters calculated from the EEG readings. Fast Fourier Transform (FFT) along with the Windowing technique is implemented. 4 different classifying models namely Support Vector Machine (SVM), Adaboost, Decision Tree, and Random Forest, have been compared. The proposed method accurately classifies hook and span-hand gestures. The Random Forest classifier achieved the highest accuracy of 78.62% followed by Decision Tree and Adaboost.
{"title":"An Electroencephalogram Based Detection of Hook and Span Hand Gestures","authors":"S. Shilaskar, Shreyas Talwekar, S. Bhatlawande, Sumitsaurabh Singh, R. Jalnekar","doi":"10.1109/PuneCon55413.2022.10014983","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014983","url":null,"abstract":"Brain-Computer Interfaces (BCI) make use of Electroencephalogram (EEG) signals to classify limb movements and other motor activities in various biomedical applications. This paper presents an EEG-based system to distinguish span and hook hand gestures. The proposed model consists of various signal processing techniques to extract features of interest and machine learning-based classification algorithms. We have extracted features based on statistical parameters calculated from the EEG readings. Fast Fourier Transform (FFT) along with the Windowing technique is implemented. 4 different classifying models namely Support Vector Machine (SVM), Adaboost, Decision Tree, and Random Forest, have been compared. The proposed method accurately classifies hook and span-hand gestures. The Random Forest classifier achieved the highest accuracy of 78.62% followed by Decision Tree and Adaboost.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114973445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brain tumors, in medical terms, are the intentional or unintentional growth of mass cells which hamper the conventional functioning of the shape of a brain. For correct diagnosis and efficient treatment planning, it is necessary to detect the brain tumor in the early stages. The tumor within the brain is one of the most dangerous diseases and might be diagnosed easily and reliably with the assistance of detection of the tumor using automated techniques on MRI Images. Positron Emission Tomography, Cerebral Arteriogram, spinal tap, and Molecular testing are used for tumor detection. Digital image processing plays an important role in the analysis of medical images. Segmentation of tumors involves the separation of abnormal brain tissues from normal tissues of the brain. Over the few past years, various researchers have proposed semi and fully-automatic methods for the detection and segmentation of Brain tumors. The motivation behind the paper is to detect neoplasm and supply the better treatment for the suffering. The objectives of the paper are to develop an end-product (Web Application) that can be installed at hospitals. To facilitate this a detection model is developed that may accurately predict if an uploaded MRI scan of the brain shows it is affected by a tumor or not. To implement the paper a Convolutional Neural Network(CNN) was used to define the model. Transfer Learning is implemented to efficiently train the model. The data set used is split into 3 sets which are train, test and validation, in the ratio 80:10:10. The model is meant to be trained for 12 epochs. Callbacks also have been given to automate the model save process. The test accuracy of 97% is achieved. This trained model will be connected with an online Application via API. Within the proposed Web App the user is having access to four routes; which is a welcome page and which contains information about the system, the second route is information and awareness about the brain tumor in medical terms, third is the detection page, in which the trained model is deployed. The user can provide an input image, MRI images in our case, and the last route is the team information. Images which are fed to the model route will be processed by the developed convolutional neural network which can then confirm if a tumor is present or not and intimidate the user for the same through an output Display. The advantage of using this system is that it will automate the detection process, and ease the workload of the hospital staff. However for the advantage to become a reality, careful selection of accurate data is needed, or else there is a chance of false results.
{"title":"Brain Tumor Detection System using Convolutional Neural Network","authors":"Shubham Koshti, Varsha N. Degaonkar, Ishan Modi, Ishan Srivastava, Janhavi Panambor, Anjali Jagtap","doi":"10.1109/PuneCon55413.2022.10014714","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014714","url":null,"abstract":"Brain tumors, in medical terms, are the intentional or unintentional growth of mass cells which hamper the conventional functioning of the shape of a brain. For correct diagnosis and efficient treatment planning, it is necessary to detect the brain tumor in the early stages. The tumor within the brain is one of the most dangerous diseases and might be diagnosed easily and reliably with the assistance of detection of the tumor using automated techniques on MRI Images. Positron Emission Tomography, Cerebral Arteriogram, spinal tap, and Molecular testing are used for tumor detection. Digital image processing plays an important role in the analysis of medical images. Segmentation of tumors involves the separation of abnormal brain tissues from normal tissues of the brain. Over the few past years, various researchers have proposed semi and fully-automatic methods for the detection and segmentation of Brain tumors. The motivation behind the paper is to detect neoplasm and supply the better treatment for the suffering. The objectives of the paper are to develop an end-product (Web Application) that can be installed at hospitals. To facilitate this a detection model is developed that may accurately predict if an uploaded MRI scan of the brain shows it is affected by a tumor or not. To implement the paper a Convolutional Neural Network(CNN) was used to define the model. Transfer Learning is implemented to efficiently train the model. The data set used is split into 3 sets which are train, test and validation, in the ratio 80:10:10. The model is meant to be trained for 12 epochs. Callbacks also have been given to automate the model save process. The test accuracy of 97% is achieved. This trained model will be connected with an online Application via API. Within the proposed Web App the user is having access to four routes; which is a welcome page and which contains information about the system, the second route is information and awareness about the brain tumor in medical terms, third is the detection page, in which the trained model is deployed. The user can provide an input image, MRI images in our case, and the last route is the team information. Images which are fed to the model route will be processed by the developed convolutional neural network which can then confirm if a tumor is present or not and intimidate the user for the same through an output Display. The advantage of using this system is that it will automate the detection process, and ease the workload of the hospital staff. However for the advantage to become a reality, careful selection of accurate data is needed, or else there is a chance of false results.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, online test systems have adapted easily to today's technologically advanced world. Examinations are an intrinsic part of the educational process. Even though the test are conducted online the teacher has to do manual evaluation. The examinations can be classified into two main types of evaluation, objective answer and subjective answer. As of now, online evaluation is available for the objective questions, hence the manual assessment of the theory answer, is a tedious task for the teacher. The teacher checks the answer manually and gives the marks. In this paper, the literature survey of existing solution is analyzed.
{"title":"Online Examination and Evaluation System","authors":"Harshad Kumar Dandage, D. Uplaonkar, Ankita Shete, Avani Shete, Lavanya Bodele, Shruti Jadhav","doi":"10.1109/PuneCon55413.2022.10014912","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014912","url":null,"abstract":"Currently, online test systems have adapted easily to today's technologically advanced world. Examinations are an intrinsic part of the educational process. Even though the test are conducted online the teacher has to do manual evaluation. The examinations can be classified into two main types of evaluation, objective answer and subjective answer. As of now, online evaluation is available for the objective questions, hence the manual assessment of the theory answer, is a tedious task for the teacher. The teacher checks the answer manually and gives the marks. In this paper, the literature survey of existing solution is analyzed.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128889896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014872
Shilpa Sonawane, P. Malathi, B.B. Musmade
Lip reading technology is best possible solution of speech recognition in noisy environments. Lip reading is a methodology to interpret by lip movement without the involvement of audio. The accuracy of lip-reading technology is based on accurate mouth region of interest (ROI). Viola Jones algorithm is used for mouth region extraction. The accuracy by viola jones is affected by merge threshold parameter of cascade object detector. Due to incorrect threshold multiple bounding boxes appears for mouth ROI. The correct selection of merge threshold leads to single bounding box on mouth region. The technique to find appropriate threshold to extract mouth ROI is presented in this paper. The algorithm is applied on GRID and LRW dataset. Experiment is tested on both frontal and profile face video frames. The accuracy obtained on frontal face frames from GRID dataset is 100 % while 86.20% accuracy achieved with profile video frames from LRW dataset.
{"title":"An Algorithm for Auto-threshold for Mouth ROI","authors":"Shilpa Sonawane, P. Malathi, B.B. Musmade","doi":"10.1109/PuneCon55413.2022.10014872","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014872","url":null,"abstract":"Lip reading technology is best possible solution of speech recognition in noisy environments. Lip reading is a methodology to interpret by lip movement without the involvement of audio. The accuracy of lip-reading technology is based on accurate mouth region of interest (ROI). Viola Jones algorithm is used for mouth region extraction. The accuracy by viola jones is affected by merge threshold parameter of cascade object detector. Due to incorrect threshold multiple bounding boxes appears for mouth ROI. The correct selection of merge threshold leads to single bounding box on mouth region. The technique to find appropriate threshold to extract mouth ROI is presented in this paper. The algorithm is applied on GRID and LRW dataset. Experiment is tested on both frontal and profile face video frames. The accuracy obtained on frontal face frames from GRID dataset is 100 % while 86.20% accuracy achieved with profile video frames from LRW dataset.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121602402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The computer is one of the wonderful and fascinating inventions of technology and has come to significant use to humans in every sector. The existing computer technology is already advanced and modern. Even so, this proposed system will provide better ease of technology to humans. The proposed system is an Artificial Intelligence (AI) application with three combined features that are AI Virtual Mouse, Keyboard and Painter These three features (AI Virtual Mouse, Keyboard and Painter) use a common hand tracking module. Hand tracking module is a python file which has a class name Hand Detector and it contains 4 member functions that are findDistance, findHands, findPosition and fingerUp. Using this module the three features work successfully. The proposed system opens with a main window which is a GUI screen made with the help of module Tkinter. This main GUI page contains all the three features (AI Virtual Mouse, Keyboard and Painter) combined. The common libraries used for system execution are OpenCV, CVZone, numpy, autopy, mediapipe, etc.
{"title":"AI Virtual Hardware","authors":"T. Sravya, Sakshi Narendra Bhargava, Shravani S, Rugveda Bodke, Nilima Kulkarni","doi":"10.1109/PuneCon55413.2022.10014775","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014775","url":null,"abstract":"The computer is one of the wonderful and fascinating inventions of technology and has come to significant use to humans in every sector. The existing computer technology is already advanced and modern. Even so, this proposed system will provide better ease of technology to humans. The proposed system is an Artificial Intelligence (AI) application with three combined features that are AI Virtual Mouse, Keyboard and Painter These three features (AI Virtual Mouse, Keyboard and Painter) use a common hand tracking module. Hand tracking module is a python file which has a class name Hand Detector and it contains 4 member functions that are findDistance, findHands, findPosition and fingerUp. Using this module the three features work successfully. The proposed system opens with a main window which is a GUI screen made with the help of module Tkinter. This main GUI page contains all the three features (AI Virtual Mouse, Keyboard and Painter) combined. The common libraries used for system execution are OpenCV, CVZone, numpy, autopy, mediapipe, etc.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"22 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014904
Hepi Suthar, Priyanka Sharma
The SSD market has been expanding quickly in recent years. There is a spare data storage space of the spare capacity/area and over provisioned capacity as a solution to issues such as the rewriting life of SSD. Additionally, it was reported that they could recover data from locations where it was impossible to access it normally. This also implies that data restoration is more difficult with SSD than with HDD. From the standpoint of digital forensics, we examine the differences between HDD and SSD data restoration. Then, we provide a fresh approach to data extraction from SSDs with over provisioned capacity.
{"title":"Method for Extracting Data from an Overprovisioned SSD","authors":"Hepi Suthar, Priyanka Sharma","doi":"10.1109/PuneCon55413.2022.10014904","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014904","url":null,"abstract":"The SSD market has been expanding quickly in recent years. There is a spare data storage space of the spare capacity/area and over provisioned capacity as a solution to issues such as the rewriting life of SSD. Additionally, it was reported that they could recover data from locations where it was impossible to access it normally. This also implies that data restoration is more difficult with SSD than with HDD. From the standpoint of digital forensics, we examine the differences between HDD and SSD data restoration. Then, we provide a fresh approach to data extraction from SSDs with over provisioned capacity.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134646102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014790
Avinash Kumar, Prathamesh Vhatkar, Hrijul Shende, Ashitosh D. Chavan, Kaliprasad A. Mahapatro
This paper proposes a strategy to predict the accurate shuttlecock trajectory and motion planning of the badminton robot using Kalman filter and Proportional-Integral-Derivative (PID) control. A PID control is used to accurately control and hold the position of the robot in a standard indoor badminton court. The conventional Kalman Filter and its various versions are mostly used to acquire the current state of the system, but the proposed modified Kalman Filter in this paper is used to predict the accurate trajectory of the shuttlecock. The effectiveness of the proposed strategy is validated experimentally for different trajectories and motion planning.
{"title":"Real- Time Trajectory Prediction and Localization of Omni-directional Badminton Robot","authors":"Avinash Kumar, Prathamesh Vhatkar, Hrijul Shende, Ashitosh D. Chavan, Kaliprasad A. Mahapatro","doi":"10.1109/PuneCon55413.2022.10014790","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014790","url":null,"abstract":"This paper proposes a strategy to predict the accurate shuttlecock trajectory and motion planning of the badminton robot using Kalman filter and Proportional-Integral-Derivative (PID) control. A PID control is used to accurately control and hold the position of the robot in a standard indoor badminton court. The conventional Kalman Filter and its various versions are mostly used to acquire the current state of the system, but the proposed modified Kalman Filter in this paper is used to predict the accurate trajectory of the shuttlecock. The effectiveness of the proposed strategy is validated experimentally for different trajectories and motion planning.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125138106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014898
Shivaraj Karjagi, Sneha Neelappagol, S. S, Vishruth S, Veena Karjigi
The gifts from nature always help those who are suffering from the sweltering heat and glaring sunlight. Watermelon is one of the summer's most wanted fruits, but we fail to judge the ripeness level. The present work aims at categorizing the state of ripeness of watermelons using recorded tapping sounds and photographed visuals. This prevents farmers from picking immature fruit. By manually hitting the watermelon and recording the sound, the sound file dataset is produced. In the case of image processing technology, a digital camera is used to capture the textures on the watermelon's exterior layers. These images have been augmented. The data gathered will assist in assessing the watermelon's ripeness. The experiments demonstrate acoustic signal processing and image processing techniques. The watermelon datasets have been divided into ripe and unripe categories with greater accuracy of 89 percent out of 336 audio samples and 93 percent out of 4864 image samples respectively.
{"title":"Watermelon Ripeness Detector Using Signal Processing","authors":"Shivaraj Karjagi, Sneha Neelappagol, S. S, Vishruth S, Veena Karjigi","doi":"10.1109/PuneCon55413.2022.10014898","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014898","url":null,"abstract":"The gifts from nature always help those who are suffering from the sweltering heat and glaring sunlight. Watermelon is one of the summer's most wanted fruits, but we fail to judge the ripeness level. The present work aims at categorizing the state of ripeness of watermelons using recorded tapping sounds and photographed visuals. This prevents farmers from picking immature fruit. By manually hitting the watermelon and recording the sound, the sound file dataset is produced. In the case of image processing technology, a digital camera is used to capture the textures on the watermelon's exterior layers. These images have been augmented. The data gathered will assist in assessing the watermelon's ripeness. The experiments demonstrate acoustic signal processing and image processing techniques. The watermelon datasets have been divided into ripe and unripe categories with greater accuracy of 89 percent out of 336 audio samples and 93 percent out of 4864 image samples respectively.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130481971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014870
Jyoti Madake, Bhavin Shah, Mihir Rakhonde, Mohit Ramdham, S. Bhatlawande, S. Shilaskar
One of the eight most significant biodiversity hotspots, the Western Ghats of India extend from the western coast of Peninsular India inland. This paper details the use of satellite data and remote sensing techniques to investigate potential hotspots for detecting shifts in forest cover. Satellite images are important for enhancing the analysis of a large area due to their higher spectral resolution. This study includes the forest cover change in the western ghats of India from 2014 to 2022. Sahyadri ranges or western ghats are one of the most verdant and densely forested mountain ranges in India; hence, even a little shift in flora can aid in deciphering and predicting numerous topographical changes. We have utilized the Normalized Difference Vegetation Index (NDVI) for determining vegetation in a particular patch of land. The forest land cover classification has been done on into three categories like low, moderate, high vegetation as well as bare areas, and tropical forests. We evaluated the values of NDVI of every image of the dataset from 2014 to 2022 to determine the definitive change in the forest cover.
{"title":"Forest Cover Change Detection of Sahyadri Ranges, India","authors":"Jyoti Madake, Bhavin Shah, Mihir Rakhonde, Mohit Ramdham, S. Bhatlawande, S. Shilaskar","doi":"10.1109/PuneCon55413.2022.10014870","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014870","url":null,"abstract":"One of the eight most significant biodiversity hotspots, the Western Ghats of India extend from the western coast of Peninsular India inland. This paper details the use of satellite data and remote sensing techniques to investigate potential hotspots for detecting shifts in forest cover. Satellite images are important for enhancing the analysis of a large area due to their higher spectral resolution. This study includes the forest cover change in the western ghats of India from 2014 to 2022. Sahyadri ranges or western ghats are one of the most verdant and densely forested mountain ranges in India; hence, even a little shift in flora can aid in deciphering and predicting numerous topographical changes. We have utilized the Normalized Difference Vegetation Index (NDVI) for determining vegetation in a particular patch of land. The forest land cover classification has been done on into three categories like low, moderate, high vegetation as well as bare areas, and tropical forests. We evaluated the values of NDVI of every image of the dataset from 2014 to 2022 to determine the definitive change in the forest cover.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124527176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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