Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

Josselin, Didier; Louvet, Romain

doi:10.3390/ijgi8030156

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…We refer to Levin (1992), that there is no single "correct" scale for describing ecosystems, rather we shall accompany our attention to the interaction among patterns and processes on different scales. We believe that in particular objectbased image analysis (OBIA) (Blaschke, 2010;Blaschke & Merschdorf, 2014;Blaschke & Piralilou, 2018;Burnett & Blaschke, 2003;Chen, Weng, Hay, & Yinan, 2018;Georganos et al, 2018;Josselin & Louvet, 2019;Tiede, 2014), can serve as a methodology to generating a structural hierarchy at multiple scales, in particular for the image processing domain.…”

Section: Discussionmentioning

confidence: 99%

Scale matters: a survey of the concepts of scale used in spatial disciplines

Dabiri

Blaschke

2019

European Journal of Remote Sensing

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Scale is a critical factor when studying patterns and the processes that cause them. A variety of approaches have been used to define the concept of scale but confusion and ambiguities remain regarding scale types and their definitions. The objectives of this study were therefore (1) to review existing types and definitions of scale, and (2) to systematically investigate the ambiguities in scale definitions and to determine the applicability of the various scale types and definitions. Through a comprehensive literature review, we identified seven types of scales and designed a survey for the seven definitions of scale and interviewed 150 scientists. The results show that the more cartography related types of scale are relatively well known while the more abstract dimensions are less known and are most ambiguous. Based on graphical examples, participants were asked which spatial scales are most relevant for their work. Surprisingly, composite objects such as a forest stand were most relevant followed by individual objects such as single trees and, lastly, more generalized categorizes or meta-objects such as “forested area”. We have drawn some conclusions that will help to clarify the different types of scale in regard to their practical use.

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Section: Discussionmentioning

confidence: 99%

Scale matters: a survey of the concepts of scale used in spatial disciplines

Dabiri

Blaschke

2019

European Journal of Remote Sensing

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“…First, it is necessary to establish an appropriate Scale level depending on the size of the object studied in the image [ 43 ]; for example, low Scale values for small shrubs and high Scale values for large shrubs [ 44 , 45 ]. Recent advances have been oriented in developing techniques (e.g., [ 53 , 54 , 55 , 56 , 57 , 58 , 59 ]) and algorithms (e.g., [ 60 , 61 , 62 , 63 ]) to automatically find the optimal value of the Scale parameter [ 64 ], which is the most important for determining the size of the segmented objects [ 65 , 66 ]. The Shape and the Compactness parameters must be configured too.…”

Section: Methodsmentioning

confidence: 99%

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Guirado

Blanco‐Sacristán

Rodríguez‐Caballero

et al. 2021

Sensors

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Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

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“…3. Scale dependence -Following the observation that the strength of multivariate image statistics tends to degrade with smaller spatial scales due to the MAUP [88,89], it is expected that k Rr GeoFID will increase as spatial scale decreases (signaling increasing visual differences between the region as a whole and smaller subregions). However, for k rq GeoFID to be considered a reliable discriminator, the effects of the MAUP on k Rr GeoFID at smaller scales for the random synthetic shape set should not exceed the qualitative threshold considered as 'visually similar' in the literature (i.e., the two random distributions being compared are not misclassified as distinct distributions solely because of the MAUP).…”

Section: Stability -The Standard Deviations Of K Rrmentioning

confidence: 99%

Methods for Measuring Geodiversity in Large Overhead Imagery Datasets

Wesley

Matisziw

2021

IEEE Access

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Geographic variation in the appearance of objects on Earth is readily observable in remotely sensed imagery (RSI) and somewhat intuitive to understand for most peoplemany classes of objects (houses, vehicles, crop fields etc.) simply look different depending on their location. This variation has recently been shown to have important implications when training machine learning models on geotagged image datasets for specific object detection and classification tasks. For example, models trained on datasets with ethnocentric biases in image content have been shown to misclassify objects in under-sampled regions, particularly in least-developed countries. The need to evaluate the growing corpus of RSI datasets for representativeness, heterogeneity and geodiversity is therefore high; yet scalable methods for measuring these concepts are absent in the remote sensing domain. This paper introduces the first dataset analysis methods for detecting and assessing geodiversity problems in large RSI datasets, based on geospatial adaptations of the Fréchet Inception Distance and Inception Score in the deep learning framework. Geospatial Fréchet Distance is proposed as a dissimilarity measure for image features of an object class across geographic regions useful for comparing differences in object class appearance in different locations and/or spatial scales. A complementary Geospatial Inception Score is proposed to quantify heterogeneity of geographic context present in dataset labels within particular regions/locations, taking into account the labels themselves as well as their immediate surroundings. Rigorous tests of these methods on simulated RSI datasets demonstrate their stability, sensitivity, and the broad range of dataset analyses to which they can be applied.INDEX TERMS Big geodata, deep convolutional neural networks, explainable ai, geographic domain shift, GIS-remote sensing fusion, imagery interpretation, spatial data analysis This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

Cited by 11 publications

References 29 publications

Scale matters: a survey of the concepts of scale used in spatial disciplines

Scale matters: a survey of the concepts of scale used in spatial disciplines

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Methods for Measuring Geodiversity in Large Overhead Imagery Datasets

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