Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)

Aryaguna, Prama Ardha; Danoedoro, Projo

doi:10.1088/1755-1315/47/1/012042

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…Machine-based image analysis approaches, applicable for automated cadastral boundaries extraction, can be grouped into two categories: (i) pixel-based approaches (PBA) and (ii) object-based approaches, also well known as Object-Based Image Analysis (OBIA) [28]. The first approach only considers spectral value or one aspect for boundary class [43]. Thus, PBA algorithms, with the exception of state-of-the-art convolution neural network (CNN) [28,[44][45][46], may result in a "salt and pepper" map when applied to very high-resolution images [47].…”

Section: The Quest Of Automation In Cadastral Mappingmentioning

confidence: 99%

Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction

et al. 2019

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The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally has led to the experimental application of Artificial Intelligence in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within Object-Based Image Analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from very high-resolution World View-2 images, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for the machine compared to 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data. Thus, these could neither be geometrically compared with human digitisation, nor actual cadastral data from the field. The results of this study provide an updated snapshot with regards to the performance of contemporary machine-driven feature extraction techniques compared to conventional manual digitising. In our methodology, using an iterative approach of segmentation and classification, we demonstrated how to overcome the weaknesses of having undesirable segments due to intra-parcel and inter-parcel variability, when using segmentation approaches for cadastral feature delineation. We also demonstrated how we can easily implement a geometric comparison framework within the Esri’s ArcGIS software environment and firmly believe the developed methodology can be reproduced.

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Section: The Quest Of Automation In Cadastral Mappingmentioning

confidence: 99%

Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction

et al. 2019

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Integration of Machine Learning Algorithms and Time-Series Satellite Images on Land Use/Land Cover Mapping with Google Earth Engine

Logavitool

Intarat²,

Horanont³

2022

Springer Geography

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Spoil characterisation using UAV-based optical remote sensing in coal mine dumps

Thiruchittampalam,

Singh,

Banerjee

et al. 2023

Int J Coal Sci Technol

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The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line. Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil offloading continues over time. Generally, the conventional in-situ coal spoil characterisation is inefficient, laborious, hazardous, and prone to experts' observation biases. To this end, this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle (UAV) based optical remote sensing. The textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters (i.e., fabric structure and relative density/consistency) in the proposed workflow. The raw images were converted to an orthomosaic using structure from motion aided processing. Then, structural descriptors were computed per pixel to enhance feature modalities of the spoil materials. Finally, machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human intervention. The characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.

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Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)

Cited by 5 publications

References 5 publications

Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction

Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction

Integration of Machine Learning Algorithms and Time-Series Satellite Images on Land Use/Land Cover Mapping with Google Earth Engine

Spoil characterisation using UAV-based optical remote sensing in coal mine dumps

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