Extracting parcel boundaries from satellite imagery for a Land Information System

Ali, Zahir; Ahmed, Shafiq

doi:10.1109/rast.2013.6581319

Cited by 8 publications

(4 citation statements)

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“…In recent years, research has focused on determining the feasibility of using HRSI for the purpose of cost-effective cadastral boundary mapping and maintenance (cf. Babawuro and Beiji 2012, Ali and Ahmed 2013, Parida et al 2014, Rao et al 2014. The extraction of cadastral boundaries in all these cases is undertaken manually: human operators interpret, mark up and ultimately map the boundaries.…”

Section: Literature Reviewmentioning

confidence: 99%

A procedure for semi-automated cadastral boundary feature extraction from high-resolution satellite imagery

Wassie

Koeva

Bennett

et al. 2017

Journal of Spatial Science

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Section: Literature Reviewmentioning

confidence: 99%

A procedure for semi-automated cadastral boundary feature extraction from high-resolution satellite imagery

Wassie

Koeva

Bennett

et al. 2017

Journal of Spatial Science

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“…Similar work was carried out by Rao et al (1996) using LISS III data and reported that the boundaries of cadastral maps matched well with image detail with respect to various features and agricultural fields. Zahir Ali and Shafiq Ahmed (2013) land cover information helps to generate survey number wise land cover and land use pattern for the current season besides updating boundaries and their periodical subdivisions. The season and crop reports of government departments are available for districts and if this reports are generated for individual parcel, it would help the government agencies to have accurate estimation of crop area and yield in near real time.…”

Section: Resultsmentioning

confidence: 99%

Generating cadastral base for Kolathupalayam village in Tamil Nadu from high resolution LISS IV sensor data

Poornima

Jagadeeswaran

Kannan

et al. 2016

JANS

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In the present study an attempt was made to generate cadastral base from high resolution satellite image (LISS IV) and to integrate with land use land cover information. The digital cadastral map with survey number for Kolathupalayam village in Erode district of Tamil Nadu was scanned, digitized and parcels were extracted. Similarly parcels or field boundaries were digitized and extracted from satellite image and were statistically compared by area. The area obtained from both the source through digitization correlated well with a pearson correlation of 0.87 and it was significant at 5 per cent. Thus, the area comparisons from both methods are significant indicating boundaries of individual fields generated from satellite image matched well with the one generated from cadastral map. The cadastral base generated from satellite image was overlaid on the classified image (level III output) to identify and generate land cover information against each survey number. Thus, the LISS IV data can be used for the identification and extraction of cadastral boundaries with good accuracy.

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“…Characteristics of cadastral boundaries extracted from high-resolution optical remote sensors. The cadastral boundaries are derived based on (a) roads, fences and edges of agricultural fields [48]; (b) fences and hedges [24]; (c,d) crop types [41]; (e) adjacent vegetation [63] and (f) roads, foot paths, water drainage, open areas and scrubs [67]. (d) Shows the case of a nonlinear irregular boundary shape.…”

Section: Cadastral Boundary Characteristicsmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step-including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.

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Extracting parcel boundaries from satellite imagery for a Land Information System

Cited by 8 publications

References 0 publications

A procedure for semi-automated cadastral boundary feature extraction from high-resolution satellite imagery

A procedure for semi-automated cadastral boundary feature extraction from high-resolution satellite imagery

Generating cadastral base for Kolathupalayam village in Tamil Nadu from high resolution LISS IV sensor data

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

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