Densely multiscale framework for segmentation of high resolution remote sensing imagery

Bello, Inuwa Mamuda; Zhang, Ke; Su, Yu; Wang, Jingyu; Aslam, Muhammad Azeem

doi:10.1016/j.cageo.2022.105196

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Our analysis showed that very small green areas (such as the green patches on roundabouts) and linear elements (such as street alignments) have lower validation scores than pocket parks or residential gardens. To analyse these accurately, there is a need for images with a better resolution, technologies which may not be publicly available and a computational capacity which increases exponentially with the detail degree of the analysis [81].…”

Section: Methods Efficiency For Extracting Small Urban Green Areasmentioning

confidence: 99%

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Popa¹,

Onose²,

Şandric³

et al. 2022

Preprint

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The importance of small urban green areas has increased in the context of rapid urbanization and densification of the urban tissue. The analysis of these areas through remote sensing has been limited due to the low spatial resolution of freely available satellite images. We propose a timeseries analysis on 3 m resolution Planet images, using GEOBIA and vegetation indices, with the aim of extracting and assessing the quality of small urban green areas in two different climatic and biogeographical regions – temperate (Bucharest, Romania) and mediterranean (Athens, Greece). Our results have shown high accuracy (over 91%) regarding the extraction of small urban green areas in both cities, across all analysed images. The timeseries analysis showed consistency in location for around 55% of the identified surfaces throughout the entire period. The vegetation indices registered higher values in the temperate region, due to the vegetation characteristics and the planning of the two cities. For the same reasons, the increase in vegetation density and quality, as a result of the distance from the city centre and the decrease in the density of built-up areas is more obvious in Athens. The proposed method provides valuable insights in the distribution and quality of small urban green areas at city level and can represent the ground basis for many analyses, currently limited by poor spatial resolution.

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Section: Methods Efficiency For Extracting Small Urban Green Areasmentioning

confidence: 99%

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Popa¹,

Onose²,

Şandric³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Liu et al (2018a) captured different scale contexts in the output results of CNN encoders, and then continuously aggregate them in a self-cascading manner. Bello et al (2022) proposed an efficient dense multi-scale segmentation network for accurate and specialized remote real-time segmentation of RS images. designed a new backbone network, taking multi-scale problems as an entry point, which can focus on more important information of multi-scales.…”

Section: Parallel Structurementioning

confidence: 99%

Deep learning-based semantic segmentation of remote sensing images: a review

Shen

et al. 2023

Front. Ecol. Evol.

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Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of deep learning (DL) in the computer vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there remains a deficiency in the evaluation and advancement of semantic segmentation techniques for RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past 5 years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques and models based on the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, allowing them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.

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MDRANet: A multiscale dense residual attention network for magnetic resonance and nuclear medicine image fusion

Peng

et al. 2023

Biomedical Signal Processing and Control

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Densely multiscale framework for segmentation of high resolution remote sensing imagery

Cited by 7 publications

References 31 publications

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Deep learning-based semantic segmentation of remote sensing images: a review

MDRANet: A multiscale dense residual attention network for magnetic resonance and nuclear medicine image fusion

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