Use of OBIA for extraction of cadastral parcels

Khadanga, Ganesh; Jain, Kamal; Merugu, Suresh

doi:10.1109/icacci.2016.7732382

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…It was done on a satellite image from Worldview, with a 1 m resolution. This method is based on an older concept, the segmentation, which involves dividing an image into several categories: point-based algorithm, margin-based, region-based, or mixed [28]. There are 8 bands of the satellite image that were used: band 1 -Coastal blue (400-450 nm), band 2 -Blue (450-510 nm), band 3 -Green (510-625 nm), band 4 -Yellow (585-625 nm), band 5 -Red (630-690 nm), band 6 -Red-edge (705-745 nm), band -Near-IR 1 (770-895 nm) and band 8 -Near-IR 2 (860-1040 nm) [29].…”

Section: Extracting the Information About The Invasive Speciesmentioning

confidence: 99%

Methods of Including Invasive Species Research in Urban Planning – A Case Study on <i>Ailanthus altissima</i>

Pindaru¹,

Niţă²

2022

Pol. J. Environ. Stud.

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With the increased effects of urbanization, essential aspects such as green spaces or quality of residential areas were often reduced in importance in developing countries. At present green spaces in urban areas are facing problems related to invasive plant species, which have been introduced in order to mitigate potential environmental problems or to increase the landscaping values. The present research started from the existing documents on urban planning at national and local level and their consideration of invasive plant species. Based on these documents, we realized a spatial analysis of the distribution of Ailanthus altissima in an urban sample, with data generated from on-field mapping of presence, a data collector and comparison of results with a population survey. In order to collect data for analysis, 100 questionnaires were applied and a total of 1036 trees were mapped using the Survey 123 application. The results provide an overview of the presence and distribution of Ailanthus altissima in the different functional areas of the urban sample.

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Section: Extracting the Information About The Invasive Speciesmentioning

confidence: 99%

Methods of Including Invasive Species Research in Urban Planning – A Case Study on <i>Ailanthus altissima</i>

Pindaru¹,

Niţă²

2022

Pol. J. Environ. Stud.

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“…eCognition is distinguished by the high-perform segmentation and classification algorithms. The perfect power of eCognition's environment for data analysis -combine OBIA techniques with state-of-the-art Deep Learning, point cloud, merge raster and vector data within a single project for hardy analyses [14]. We can develop rule-sets and workflows for advanced segmentation to classification and feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Unmanned Aerial Vehicles (UAV)-based Cadastral Map: Case Study in AL-Shatrah District- Iraq

Difar

Abed

2022

eijs

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Cadastral map environment is directed, more than ever before, towards Artificial Intelligence use to produce fast and accurate maps and keep up with the huge population growth. The traditional approach currently in production of maps is expensive and effort-intensive in addition to be considered as highly time-consuming process. UAV-based cadastral mapping imagery that use automatic techniques are newly being exploited to accelerate the process of production or updating. The state-of-the-art intelligent algorithms are capable to extract land boundaries from images better than conventional techniques. This paper presents an automatic workflow of cadastral map production based on land boundary and automatic feature extraction from UAV-based imageries. The developed workflow involved four steps: (i) Flight planning and Ground Control Points (GCPs) observation, (ii) image pre-processing, (iii) image segmentation (iv) boundary detection and extraction (classification), (v) postprocessing, and (vi) accuracy assessment. The study area includes geometrical and affect spectral reflectivity (slums) covered by 206 images those captured using a quad-rotary UAV (DJI Phantom 4 pro). The mosaic image of the Area of Interest (AOI) was produced by following Structure from Motion /Multi-view Stereo (SfM-MVS) photogrammetry which covers 0.592 km2 ground area using pix4DMapper software. The Multi-Resolution Segmentation (MRS) algorithm was applied for object generation and later both spectral and geometrical information (area, brightness, border and the normalized digital surface model NDSM)) utilised to extract the boundaries by Object Based Image Analysis (OBIA) rule-based expert systems in eCognition software. The most exciting challenge in this AOI was buildings separation depending on number of buildings using the QGIS software. The accuracy assessment of the obtained results showed that 88% of the extracted boundaries were correct following automatic extraction routine when compared to manual digitizing. This approach can highly compensate time, efforts, and low accuracy outcomes from traditional surveying and manual registration approaches applied in Iraqi institutes nowadays. The presented approach can definitely help to speed up map production phase and keep pace up with population expansion and modern technologies.

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“…Several previous studies on cadastral mapping and updating dealt with non-spatial updates, but focused only on extracting up-to-date information from the source date. The comparing and detecting changes step of updates were dependent on visual interpretation or performed in a limited range [2,9,10,[12][13][14]. Specifically, Khadanga et al [14] classified land use in cadastral parcels extracted from high-resolution satellite imagery through object-based image analysis (OBIA).…”

Section: Introductionmentioning

confidence: 99%

“…The comparing and detecting changes step of updates were dependent on visual interpretation or performed in a limited range [2,9,10,[12][13][14]. Specifically, Khadanga et al [14] classified land use in cadastral parcels extracted from high-resolution satellite imagery through object-based image analysis (OBIA). The result layer of OBIA was written into a shapefile and compared with a digitized map of the cadastral parcels.…”

Section: Introductionmentioning

confidence: 99%

Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea

Park

Song

2020

Remote Sensing

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The non-spatial information of cadastral maps must be repeatedly updated to monitor recent changes in land property and to detect illegal land registrations by tax evaders. Since non-spatial information, such as land category, is usually updated by field-based surveys, it is time-consuming and only a limited area can be updated at a time. Although land categories can be updated by remote sensing techniques, the update is typically performed through manual analysis, namely through a visually interpreted comparison between the newly generated land information and the existing cadastral maps. A cost-effective, fast alternative to the current surveying methods would improve the efficiency of land management. For this purpose, the present study analyzes the discrepancy between the existing cadastral map and the actual land use. Our proposed method operates in two steps. First, an up-to-date land cover map is generated from hyperspectral unmanned aerial vehicle (UAV) images. These images are effectively classified by a hybrid two- and three-dimensional convolutional neural network. Second, a discrepancy map, which contains the ratio of the area that is being used differently from the registered land use in each parcel, is constructed through a three-stage inconsistency comparison. As a case study, the proposed method was evaluated using hyperspectral UAV images acquired at two sites of Jeonju in South Korea. The overall classification accuracies of six land classes at Sites 1 and 2 were 99.93% and 99.75% and those at Sites 1 and 2 are 39.4% and 34.4%, respectively, which had discrepancy ratios of 50% or higher. Finally, discrepancy maps between the land cover maps and existing cadastral maps were generated and visualized. The method automatically reveals the inconsistent parcels requiring updates of their land category. Although the performance of the proposed method depends on the classification results obtained from UAV imagery, the method allows a flexible modification of the matching criteria between the land categories and land coverage. Therefore, it is generalizable to various cadastral systems and the discrepancy ratios will provide practical information and significantly reduce the time and effort for land monitoring and field surveying.

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Use of OBIA for extraction of cadastral parcels

Cited by 6 publications

References 8 publications

Methods of Including Invasive Species Research in Urban Planning – A Case Study on <i>Ailanthus altissima</i>

Methods of Including Invasive Species Research in Urban Planning – A Case Study on <i>Ailanthus altissima</i>

Automatic Extraction of Unmanned Aerial Vehicles (UAV)-based Cadastral Map: Case Study in AL-Shatrah District- Iraq

Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea

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