Pub Date : 2023-10-19DOI: 10.5194/isprs-archives-xlviii-1-w3-2023-25-2023
L. E. Budde, J. Schmidt, T. Kullmann, D. Iwaszczuk
Abstract. Open science is an important attribute for developing new approaches. Especially, the data component plays a significant role. The FAIR principle provides a good orientation towards open data. One part of FAIR is findability. Thus, domain specific dataset search platforms were developed: the Earth Observation Database and our Benchmark Metadata Database (BeMeDa). In addition to the search itself, the datasets found by this platforms can be compared with each other with regard to their interoperability. We compare these two platforms and present an update of our platform BeMeDa. This update includes additional location information about the datasets and a new frontend design with improved usability. We rely on user feedback for further improvements and enhancements.
{"title":"CURRENT STATUS OF THE BENCHMARK DATABASE BEMEDA","authors":"L. E. Budde, J. Schmidt, T. Kullmann, D. Iwaszczuk","doi":"10.5194/isprs-archives-xlviii-1-w3-2023-25-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-25-2023","url":null,"abstract":"Abstract. Open science is an important attribute for developing new approaches. Especially, the data component plays a significant role. The FAIR principle provides a good orientation towards open data. One part of FAIR is findability. Thus, domain specific dataset search platforms were developed: the Earth Observation Database and our Benchmark Metadata Database (BeMeDa). In addition to the search itself, the datasets found by this platforms can be compared with each other with regard to their interoperability. We compare these two platforms and present an update of our platform BeMeDa. This update includes additional location information about the datasets and a new frontend design with improved usability. We rely on user feedback for further improvements and enhancements.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135730462","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 : 2023-10-17DOI: 10.5194/isprs-archives-xlviii-m-3-2023-299-2023
R. Tamimi, C. Toth
Abstract. Digital documentation of historical sites has always required the use of expensive professional grade sensors capable of collecting large amounts of data to reconstruct cultural sites. These types of projects generally require large budgets and a large team of specialists to successfully generate a digital model. However, with smart devices having sensors capable of mapping on the go, the potential for mapping such historical sites may be more accessible. This study aims to conduct a comprehensive comparison between the iPhone 13 Pro and the Unmanned Aerial Systems (UAS) photogrammetric model of the Great Pyramid of Giza, otherwise known as the Khufu pyramid, located in Giza, Egypt. The purpose of this study is to evaluate the potential of the iPhone 13 Pro's Camera and LiDAR sensor capabilities as a valuable tool for documenting and preserving cultural heritage sites. To accomplish this, data was captured from multiple positions around the pyramid using the Pix4Dcatch app on the iPhone 13 Pro, and the data was processed using Pix4Dmatic to generate a 3D point cloud of the pyramid. This point cloud data is then compared to the reference data obtained through the UAS mapping which generated a 3D photogrammetric model. The comparison aims to identify the strengths and weaknesses of using the iPhone 13 Pro for this type of scanning and to assess the accuracy and precision of the generated data.
{"title":"COMPARISON OF IPHONE 13 PRO'S CAMERA AND LIDAR SENSOR TO UAS PHOTOGRAMMETRIC MODEL OF THE GREAT PYRAMID OF GIZA","authors":"R. Tamimi, C. Toth","doi":"10.5194/isprs-archives-xlviii-m-3-2023-299-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-299-2023","url":null,"abstract":"Abstract. Digital documentation of historical sites has always required the use of expensive professional grade sensors capable of collecting large amounts of data to reconstruct cultural sites. These types of projects generally require large budgets and a large team of specialists to successfully generate a digital model. However, with smart devices having sensors capable of mapping on the go, the potential for mapping such historical sites may be more accessible. This study aims to conduct a comprehensive comparison between the iPhone 13 Pro and the Unmanned Aerial Systems (UAS) photogrammetric model of the Great Pyramid of Giza, otherwise known as the Khufu pyramid, located in Giza, Egypt. The purpose of this study is to evaluate the potential of the iPhone 13 Pro's Camera and LiDAR sensor capabilities as a valuable tool for documenting and preserving cultural heritage sites. To accomplish this, data was captured from multiple positions around the pyramid using the Pix4Dcatch app on the iPhone 13 Pro, and the data was processed using Pix4Dmatic to generate a 3D point cloud of the pyramid. This point cloud data is then compared to the reference data obtained through the UAS mapping which generated a 3D photogrammetric model. The comparison aims to identify the strengths and weaknesses of using the iPhone 13 Pro for this type of scanning and to assess the accuracy and precision of the generated data.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992923","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 : 2023-10-17DOI: 10.5194/isprs-archives-xlviii-m-3-2023-307-2023
R. Tamimi, C. Toth
Abstract. In this study, we investigate the feasibility of using an iPhone 14 Pro's camera and LiDAR sensors to collect high-accuracy spatial data on a mobile e-scooter. Given the widespread availability of e-scooters in urban areas, they present an ideal platform for creating a compact mobile mapping system. The iPhone is securely mounted on the e-scooter and paired with a viDoc RTK Rover, which offers real-time kinematic (RTK) positioning accuracy in open sky areas. As the e-scooter traverses the area of interest, data is collected using the LiDAR sensor, while images are captured using the camera. The collected data is then processed using Pix4Dmatic software, enabling the generation of a fused point cloud and a detailed digital model of the surveyed area. In situations where the Global Navigation Satellite System (GNSS) signal is compromised or unavailable, such as indoor environments or urban canyons, alternative methods like Simultaneous Localization and Mapping (SLAM) can be employed. Additionally, Total Stations can be utilized to track the entire system's movement in GNSS-denied environments and provide accurate georeferencing for the acquired data. Control and check points throughout the area of interest are established using the Total Station as well. This approach offers a flexible and cost-effective means of collecting high-accuracy spatial data in small areas across a variety of environments, leveraging the readily available e-scooters for public use. The results of various experiments conducted using an iPhone 14 Pro and viDoc RTK on an e-scooter are thoroughly analyzed and reported in this paper.
{"title":"PERFORMANCE ASSESSMENT OF A MINI MOBILE MAPPING SYSTEM: IPHONE 14 PRO INSTALLED ON A E-SCOOTER","authors":"R. Tamimi, C. Toth","doi":"10.5194/isprs-archives-xlviii-m-3-2023-307-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-307-2023","url":null,"abstract":"Abstract. In this study, we investigate the feasibility of using an iPhone 14 Pro's camera and LiDAR sensors to collect high-accuracy spatial data on a mobile e-scooter. Given the widespread availability of e-scooters in urban areas, they present an ideal platform for creating a compact mobile mapping system. The iPhone is securely mounted on the e-scooter and paired with a viDoc RTK Rover, which offers real-time kinematic (RTK) positioning accuracy in open sky areas. As the e-scooter traverses the area of interest, data is collected using the LiDAR sensor, while images are captured using the camera. The collected data is then processed using Pix4Dmatic software, enabling the generation of a fused point cloud and a detailed digital model of the surveyed area. In situations where the Global Navigation Satellite System (GNSS) signal is compromised or unavailable, such as indoor environments or urban canyons, alternative methods like Simultaneous Localization and Mapping (SLAM) can be employed. Additionally, Total Stations can be utilized to track the entire system's movement in GNSS-denied environments and provide accurate georeferencing for the acquired data. Control and check points throughout the area of interest are established using the Total Station as well. This approach offers a flexible and cost-effective means of collecting high-accuracy spatial data in small areas across a variety of environments, leveraging the readily available e-scooters for public use. The results of various experiments conducted using an iPhone 14 Pro and viDoc RTK on an e-scooter are thoroughly analyzed and reported in this paper.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135995191","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 : 2023-09-07DOI: 10.5194/isprs-archives-xlviii-m-3-2023-293-2023
A. Bala, T. Youngu, S. Azua, A. O. Aliyu, S. Yabo, A. U. Aliyu
Abstract. The Boko Haram insurgency has had a significant impact on the Monguno Local Government Area (LGA) in Borno State, Nigeria, for more than ten years. This study analysed how the Boko Haram insurgency affected the built-up areas of Monguno LGA. The focus of this study was to map, analyse, and detect the spectral and spatial changes of the earth's surface using remotely sensed images and geospatial techniques, focusing in particular on the built-up areas in the study area, in order to provide sufficient information on the status of built-up areas for effective planning and good governance. Employing a combination of the pixel-based Supervised Maximum Likelihood classification algorithm and the Object Based Image analysis, the study used Landsat 7 ETM+ satellite imageries for the years 2004 and 2007 and Landsat 8 OLT/TIRS imageries for the years 2014 and 2021 to determine the rate of change in the built-up areas over a period of seventeen (17) years (from 2004 to 2021). The classified Land Use and Land Cover (LULC) maps were grouped into four classes: water body, built-up areas, vegetation, and bare land, even though the study was more concerned about the changes in the built-up area. The results showed that from 2004, the built-up area occupied 0.12% with a total land area of 2.00km2 and increased in 2007 by 0.21%. From 2007 to 2014 the built-up area was seen to have increased by 0.31% with a built-up area of 6.00km2. Similarly, there was an increase in the built-up area from 0.31% in 2014 to 0.63% in 2021. Generally, the built-up area has increased by 0.51% from 2004–2021 and the largest percentage increase was noticed from 2014 to 2021 where there was an increase of 0.31% in the built-up area. This increase signifies that there has been an inflow of people into Monguno from neighbouring LGA. It was recommended that future research should incorporate other parameters such as population, literacy level, and socioeconomic well-being of the people.
{"title":"BUILT-UP AREA DYNAMICS IN “PRE- AND DURING BOKO HARAM CONFLICTS” IN MONGUNO LOCAL GOVERNMENT AREA OF BORNO STATE, NIGERIA","authors":"A. Bala, T. Youngu, S. Azua, A. O. Aliyu, S. Yabo, A. U. Aliyu","doi":"10.5194/isprs-archives-xlviii-m-3-2023-293-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-293-2023","url":null,"abstract":"Abstract. The Boko Haram insurgency has had a significant impact on the Monguno Local Government Area (LGA) in Borno State, Nigeria, for more than ten years. This study analysed how the Boko Haram insurgency affected the built-up areas of Monguno LGA. The focus of this study was to map, analyse, and detect the spectral and spatial changes of the earth's surface using remotely sensed images and geospatial techniques, focusing in particular on the built-up areas in the study area, in order to provide sufficient information on the status of built-up areas for effective planning and good governance. Employing a combination of the pixel-based Supervised Maximum Likelihood classification algorithm and the Object Based Image analysis, the study used Landsat 7 ETM+ satellite imageries for the years 2004 and 2007 and Landsat 8 OLT/TIRS imageries for the years 2014 and 2021 to determine the rate of change in the built-up areas over a period of seventeen (17) years (from 2004 to 2021). The classified Land Use and Land Cover (LULC) maps were grouped into four classes: water body, built-up areas, vegetation, and bare land, even though the study was more concerned about the changes in the built-up area. The results showed that from 2004, the built-up area occupied 0.12% with a total land area of 2.00km2 and increased in 2007 by 0.21%. From 2007 to 2014 the built-up area was seen to have increased by 0.31% with a built-up area of 6.00km2. Similarly, there was an increase in the built-up area from 0.31% in 2014 to 0.63% in 2021. Generally, the built-up area has increased by 0.51% from 2004–2021 and the largest percentage increase was noticed from 2014 to 2021 where there was an increase of 0.31% in the built-up area. This increase signifies that there has been an inflow of people into Monguno from neighbouring LGA. It was recommended that future research should incorporate other parameters such as population, literacy level, and socioeconomic well-being of the people.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42138951","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 : 2023-09-06DOI: 10.5194/isprs-archives-xlviii-m-3-2023-285-2023
S. Mamgain, H. C. Karnatak, A. Roy
Abstract. Burnt area assessment due to forest fires is an important aspect to estimate the extent of loss of biodiversity which has become feasible even in hilly and inaccessible areas with the help of geospatial technologies. But satellite data also has some limitations as it increases commission error by misclassifying non-burnt areas as burnt areas. To reduce this commission error, present study has attempted to integrate multi-sensor satellite data to characterize and extract forest fire burnt areas in Uttarakhand which is a fire prone hilly state in Western Himalaya. Landsat-8 and Sentinel-2 optical datasets have been used to calculate eleven vegetation/burn indices to identify burn patches for fire season of 2022 (February to June). These vegetation/burn indices have been calculated from Landsat-8 and Sentinel-2 datasets and integrated using Fuzzy Logic Modelling to get characterized forest fire burnt area maps. Accuracy assessment has been done using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points for the characterized map of burnt area by Landsat-8, Sentinel-2 and combining indices from both sensors. The fuzzy map of burnt area using Landsat-8 showed the accuracy of 66.25%, while Sentinel-2 showed accuracy of 59.79% and the integration of fuzzy burnt area maps of both sensors showed the highest accuracy of 79.66%. This information of characterized burnt areas of a region can help forest managers to identify high vulnerable areas to focus on during the fire season to prevent the losses to natural resources, life and property in the region.
{"title":"FOREST FIRE BURNT AREA EXTRACTION USING FUZZY INTEGRATION OF MULTI-SENSOR SATELLITE DATA FOR THE HIMALAYAN STATE","authors":"S. Mamgain, H. C. Karnatak, A. Roy","doi":"10.5194/isprs-archives-xlviii-m-3-2023-285-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-285-2023","url":null,"abstract":"Abstract. Burnt area assessment due to forest fires is an important aspect to estimate the extent of loss of biodiversity which has become feasible even in hilly and inaccessible areas with the help of geospatial technologies. But satellite data also has some limitations as it increases commission error by misclassifying non-burnt areas as burnt areas. To reduce this commission error, present study has attempted to integrate multi-sensor satellite data to characterize and extract forest fire burnt areas in Uttarakhand which is a fire prone hilly state in Western Himalaya. Landsat-8 and Sentinel-2 optical datasets have been used to calculate eleven vegetation/burn indices to identify burn patches for fire season of 2022 (February to June). These vegetation/burn indices have been calculated from Landsat-8 and Sentinel-2 datasets and integrated using Fuzzy Logic Modelling to get characterized forest fire burnt area maps. Accuracy assessment has been done using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points for the characterized map of burnt area by Landsat-8, Sentinel-2 and combining indices from both sensors. The fuzzy map of burnt area using Landsat-8 showed the accuracy of 66.25%, while Sentinel-2 showed accuracy of 59.79% and the integration of fuzzy burnt area maps of both sensors showed the highest accuracy of 79.66%. This information of characterized burnt areas of a region can help forest managers to identify high vulnerable areas to focus on during the fire season to prevent the losses to natural resources, life and property in the region.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47617996","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-87-2023
A. Elashry, C. Toth
Abstract. Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.
{"title":"IMPROVING CAMERA POSE ESTIMATION USING SWARM PARTICLE ALGORITHMS","authors":"A. Elashry, C. Toth","doi":"10.5194/isprs-archives-xlviii-m-3-2023-87-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-87-2023","url":null,"abstract":"Abstract. Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48414765","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-189-2023
D. Prabhakar, P. K. Garg
Abstract. Detection of Building edges is crucial for building information extraction and description. Extracting structures from large-scale aerial images has been utilized for years in cartography. With commercially available high-resolution satellites, many aerial photography usages can now employ satellite imagery. Edge detection is focused on pinpointing distinct transitions between greyscale image regions and attributing their origins to underlying physical processes. Detecting building boundaries from very high-resolution (VHR) remote sensing data is essential for many geo-related applications, such as urban planning and management, surveying and mapping, 3D reconstruction, motion recognition, image registration, image enhancement and restoration, image compression, and more. The rapid evolution of convolutional neural networks (CNNs) has led to substantial breakthroughs in edge detection in recent years. Sharp, localized changes in brightness characterize edges in digital images. In most cases, edge detection requires some kind of image smoothing and separation. Differentiation is an ill-conditioned problem, and smoothing leads to information loss. It is challenging to create an edge detection method that works everywhere and adapts to any future processing stages. Therefore, throughout the development of digital image processing, numerous edge detectors have been created, each with its own unique set of mathematical and algorithmic properties. Several edge detectors have been developed due to application needs and the subjective nature of edge definition and characterization. We propose a deep learning technique, particularly convolutional neural networks(CNNs), that offers a promising approach to automatically learn and extract features from very high-resolution remote sensing imagery, leading to more accurate and efficient building edge detection.
{"title":"BUILDING EDGE DETECTION FROM VERY HIGH-RESOLUTION REMOTE SENSING IMAGERY USING DEEP LEARNING","authors":"D. Prabhakar, P. K. Garg","doi":"10.5194/isprs-archives-xlviii-m-3-2023-189-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-189-2023","url":null,"abstract":"Abstract. Detection of Building edges is crucial for building information extraction and description. Extracting structures from large-scale aerial images has been utilized for years in cartography. With commercially available high-resolution satellites, many aerial photography usages can now employ satellite imagery. Edge detection is focused on pinpointing distinct transitions between greyscale image regions and attributing their origins to underlying physical processes. Detecting building boundaries from very high-resolution (VHR) remote sensing data is essential for many geo-related applications, such as urban planning and management, surveying and mapping, 3D reconstruction, motion recognition, image registration, image enhancement and restoration, image compression, and more. The rapid evolution of convolutional neural networks (CNNs) has led to substantial breakthroughs in edge detection in recent years. Sharp, localized changes in brightness characterize edges in digital images. In most cases, edge detection requires some kind of image smoothing and separation. Differentiation is an ill-conditioned problem, and smoothing leads to information loss. It is challenging to create an edge detection method that works everywhere and adapts to any future processing stages. Therefore, throughout the development of digital image processing, numerous edge detectors have been created, each with its own unique set of mathematical and algorithmic properties. Several edge detectors have been developed due to application needs and the subjective nature of edge definition and characterization. We propose a deep learning technique, particularly convolutional neural networks(CNNs), that offers a promising approach to automatically learn and extract features from very high-resolution remote sensing imagery, leading to more accurate and efficient building edge detection.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42367327","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-255-2023
R. Virtriana, T. S. Anggraini, Kalingga Titon, Nur Ihsan, Dyah Ayu, Retnowati, Pitri Rohayani, A. B. Harto
Abstract. The Javan rhinoceros (Rhinoceros sondaicus) is one of the endemic animals in Java, Indonesia, which is currently threatened with extinction and is included in the 25 species program as the top priority for the Indonesian government. In 2021 the Indonesian Ministry of Environment and Forestry said that only 75 Javan rhinos remained in Ujung Kulon National Park in Banten Province. Ujung Kulon National Park is the primary habitat of the Javan rhino, so it requires special attention to protect this habitat. One of the reasons for the reduced population of the Javan rhinoceros is the diminishing availability of habitat. Habitat reduction occurs due to changes in land cover due to human activities. This study aims to identify changes in the habitat suitability of the Javan rhinoceros due to human pressure. Parameters of human pressure will be identified using changes in land cover in 2000 and 2018. Remote sensing and GIS technology will be used to monitor habitat suitability for endemic animals over a large area and a long time. The Javan rhino habitat suitability analysis in 2000 and 2018 will integrate geographical, environmental, and meteorological parameters. The MCDA (Multi-Criteria Decision Analysis) method will determine a decision from several suitability parameters. Based on observations of human activities parameters, there have been significant changes to land cover from 2000–2018, especially in residential areas, which negatively impacted the suitability of the Javan Rhino's habitat. The results of this study can identify priority areas that require protective action for the Javan Rhinoceros habitat. This research is expected to be the basis for protecting endangered endemic animals, especially the Javan Rhinoceros, so their habitat is preserved.
{"title":"THE LAND COVER CHANGE EFFECT FOR JAVAN RHINOCEROS SITE SUITABILITY","authors":"R. Virtriana, T. S. Anggraini, Kalingga Titon, Nur Ihsan, Dyah Ayu, Retnowati, Pitri Rohayani, A. B. Harto","doi":"10.5194/isprs-archives-xlviii-m-3-2023-255-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-255-2023","url":null,"abstract":"Abstract. The Javan rhinoceros (Rhinoceros sondaicus) is one of the endemic animals in Java, Indonesia, which is currently threatened with extinction and is included in the 25 species program as the top priority for the Indonesian government. In 2021 the Indonesian Ministry of Environment and Forestry said that only 75 Javan rhinos remained in Ujung Kulon National Park in Banten Province. Ujung Kulon National Park is the primary habitat of the Javan rhino, so it requires special attention to protect this habitat. One of the reasons for the reduced population of the Javan rhinoceros is the diminishing availability of habitat. Habitat reduction occurs due to changes in land cover due to human activities. This study aims to identify changes in the habitat suitability of the Javan rhinoceros due to human pressure. Parameters of human pressure will be identified using changes in land cover in 2000 and 2018. Remote sensing and GIS technology will be used to monitor habitat suitability for endemic animals over a large area and a long time. The Javan rhino habitat suitability analysis in 2000 and 2018 will integrate geographical, environmental, and meteorological parameters. The MCDA (Multi-Criteria Decision Analysis) method will determine a decision from several suitability parameters. Based on observations of human activities parameters, there have been significant changes to land cover from 2000–2018, especially in residential areas, which negatively impacted the suitability of the Javan Rhino's habitat. The results of this study can identify priority areas that require protective action for the Javan Rhinoceros habitat. This research is expected to be the basis for protecting endangered endemic animals, especially the Javan Rhinoceros, so their habitat is preserved.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44793249","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-169-2023
J. Oppong, Z. H. Ning, Y. Twumasi, R. A. Antwi, M. Anokye, G. Ahoma, J. Annan, J. Namwamba, P. Loh, C. Akinrinwoye
Abstract. Urban ecosystems face numerous challenges due to rapid urbanization and population growth. Effective management of these ecosystems is crucial to ensure their sustainability and the well-being of urban residents. Remote sensing (RS) and Geographic Information Systems (GIS) have emerged as valuable tools for understanding and managing urban ecosystems. The integration of remote sensing and GIS technologies facilitate the monitoring and assessment of urban biodiversity, aiding in the conservation and restoration of ecological habitats. With this mind, the objective of this study was to investigate the integration of remote sensing and GIS technologies for real-time monitoring and assessment of environmental parameters in urban ecosystems, and their role in supporting sustainable urban ecosystem conservation efforts. Landsat 8 Operational Land Imager (OLI) images were acquired between January 2nd and April 5th 2020 to assess and monitor the dynamics in urban ecosystems in Abidjan, Accra, and Lagos. The Normalized Difference Built-up index was used to detect areas covered with concrete structures and impervious surfaces, while the Normalized Difference Vegetation Index and Normalized Difference Water Index were used to detect areas covered with vegetation and water bodies, respectively. Results of the study show that Abidjan, Accra, and Lagos experienced increased built-up areas at the expense of other land uses such as forests. Remote Sensing and GIS technologies provide valuable insights into the spatial and temporal dynamics of urban environments, supporting evidence-based decision-making and sustainable urban planning and development.
{"title":"THE INTEGRATION OF REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM (GIS) IN MANAGING URBAN ECOSYSTEMS","authors":"J. Oppong, Z. H. Ning, Y. Twumasi, R. A. Antwi, M. Anokye, G. Ahoma, J. Annan, J. Namwamba, P. Loh, C. Akinrinwoye","doi":"10.5194/isprs-archives-xlviii-m-3-2023-169-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-169-2023","url":null,"abstract":"Abstract. Urban ecosystems face numerous challenges due to rapid urbanization and population growth. Effective management of these ecosystems is crucial to ensure their sustainability and the well-being of urban residents. Remote sensing (RS) and Geographic Information Systems (GIS) have emerged as valuable tools for understanding and managing urban ecosystems. The integration of remote sensing and GIS technologies facilitate the monitoring and assessment of urban biodiversity, aiding in the conservation and restoration of ecological habitats. With this mind, the objective of this study was to investigate the integration of remote sensing and GIS technologies for real-time monitoring and assessment of environmental parameters in urban ecosystems, and their role in supporting sustainable urban ecosystem conservation efforts. Landsat 8 Operational Land Imager (OLI) images were acquired between January 2nd and April 5th 2020 to assess and monitor the dynamics in urban ecosystems in Abidjan, Accra, and Lagos. The Normalized Difference Built-up index was used to detect areas covered with concrete structures and impervious surfaces, while the Normalized Difference Vegetation Index and Normalized Difference Water Index were used to detect areas covered with vegetation and water bodies, respectively. Results of the study show that Abidjan, Accra, and Lagos experienced increased built-up areas at the expense of other land uses such as forests. Remote Sensing and GIS technologies provide valuable insights into the spatial and temporal dynamics of urban environments, supporting evidence-based decision-making and sustainable urban planning and development.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41329304","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-261-2023
R. Virtriana, D. Retnowati, Pitri Rohayani, T. S. Anggraini, Kalingga Titon, Nur Ihsan, A. B. Harto, A. Riqqi
Abstract. Flood is one of the natural disasters which has a high intensity in terms of occurrence. Despite the loss value in each event which is not as high as some natural disasters, such as earthquakes or tsunamis, the high occurrence of floods may cause high loss in total. Floods damage property and infrastructure, disrupt economic activity, displace people, harm communities, and degrade ecosystems. This study aims to compare the flood hazard model using Geomorphic Flood Index (GFI) and Multi-criteria Decision Analysis (MCDA). GFI is an established method which already used globally to identify the flood-prone area and the depth of the flood. The parameter needed to calculate GFI are the elevation, river network, and historical flood event. Meanwhile, the MCDA method tries to combine environmental, physical, and hydrographic factors, such as land use/land cover, precipitation, and runoff. The study area is Jatinangor District in Sumedang Regency which part of West Java Province, Indonesia. This location is chosen based on historical and potential flood events. Besides, Jatinangor District is the center of industry and commerce which Sumedang Regency is very dependent on. The finding of this study is expected to identify suitable methods for assessing flood hazards in Jatinangor or other areas with similar characteristics, between GFI and MCDA.
{"title":"COMPARATIVE ANALYSIS OF THE MCDA AND GFI METHODS IN DETERMINING FLOOD-PRONE AREAS IN JATINANGOR DISTRICT, SUMEDANG","authors":"R. Virtriana, D. Retnowati, Pitri Rohayani, T. S. Anggraini, Kalingga Titon, Nur Ihsan, A. B. Harto, A. Riqqi","doi":"10.5194/isprs-archives-xlviii-m-3-2023-261-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-261-2023","url":null,"abstract":"Abstract. Flood is one of the natural disasters which has a high intensity in terms of occurrence. Despite the loss value in each event which is not as high as some natural disasters, such as earthquakes or tsunamis, the high occurrence of floods may cause high loss in total. Floods damage property and infrastructure, disrupt economic activity, displace people, harm communities, and degrade ecosystems. This study aims to compare the flood hazard model using Geomorphic Flood Index (GFI) and Multi-criteria Decision Analysis (MCDA). GFI is an established method which already used globally to identify the flood-prone area and the depth of the flood. The parameter needed to calculate GFI are the elevation, river network, and historical flood event. Meanwhile, the MCDA method tries to combine environmental, physical, and hydrographic factors, such as land use/land cover, precipitation, and runoff. The study area is Jatinangor District in Sumedang Regency which part of West Java Province, Indonesia. This location is chosen based on historical and potential flood events. Besides, Jatinangor District is the center of industry and commerce which Sumedang Regency is very dependent on. The finding of this study is expected to identify suitable methods for assessing flood hazards in Jatinangor or other areas with similar characteristics, between GFI and MCDA.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49298143","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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