Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle
This research aims to understand the applicability of Python-based Convolutional Neural Networks (CNNs) in combination with satellite imageries to classify the landscape types and create the Landscape Ecology Triangle for Bangladesh. Prevailing as a field that involves the manual interpretation and...
Gorde:
| Egile Nagusiak: | , , , |
|---|---|
| Formatua: | Online |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Jahangirnagar University
2025
|
| Gaiak: | |
| Sarrera elektronikoa: | http://journals.juniv.edu/index.php/tjr/article/view/211 |
| Etiketak: |
Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
|
| _version_ | 1873958286481424384 |
|---|---|
| author | Tazrian, Abu Tanvir Mohammad Meherin, Samira Toma, Tania Islam Mahmud, Khandakar Hasan |
| author_facet | Tazrian, Abu Tanvir Mohammad Meherin, Samira Toma, Tania Islam Mahmud, Khandakar Hasan |
| author_sort | Tazrian, Abu Tanvir Mohammad |
| collection | OJS |
| description | This research aims to understand the applicability of Python-based Convolutional Neural Networks (CNNs) in combination with satellite imageries to classify the landscape types and create the Landscape Ecology Triangle for Bangladesh. Prevailing as a field that involves the manual interpretation and empirical observation of landscapes, landscape ecology is becoming infused with deep learning, thus creating new approaches to the analysis of spatial data. Unlike previous research that has concentrated on the characterization of landscape, this research classifies seven types of landscape: City, District Town, Upazila Town, Paurasabha, Urban Fringe, Rural Fringe, and Rural. This study also used secondary data from the World Bank databases and other reliable sources, in addition to satellite imageries, to classify and describe the landscape types. Data about the proportions of the landscape elements especially the built-up areas were collected and compiled from a literature review that included the relevant research articles. These data were used to train a specific CNN model that was intended to identify landscapes according to their ecological and structural properties. The derived Landscape Ecology Triangle offers a conceptual model that not only categorizes different types of landscapes but also quantifies the proportion of various landscape components within each type, thereby offering a better understanding of the landscape processes. When applied to the classification of landscape types, the approach can give an understanding of the best places for living based on ecological conditions and the suitability of the environment, thus promoting further ecological studies and better practices of land use in Bangladesh. |
| format | Online |
| id | ojs-211 |
| institution | My University |
| language | eng |
| publishDate | 2025 |
| publisher | Jahangirnagar University |
| record_format | ojs |
| spelling | ojs-2112025-10-16T03:25:54Z Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle Tazrian, Abu Tanvir Mohammad Meherin, Samira Toma, Tania Islam Mahmud, Khandakar Hasan Landscape Ecology Triangle Landscape Types Landscape Elements CNN Model Suitability of the Environment Deep Learning in Ecology This research aims to understand the applicability of Python-based Convolutional Neural Networks (CNNs) in combination with satellite imageries to classify the landscape types and create the Landscape Ecology Triangle for Bangladesh. Prevailing as a field that involves the manual interpretation and empirical observation of landscapes, landscape ecology is becoming infused with deep learning, thus creating new approaches to the analysis of spatial data. Unlike previous research that has concentrated on the characterization of landscape, this research classifies seven types of landscape: City, District Town, Upazila Town, Paurasabha, Urban Fringe, Rural Fringe, and Rural. This study also used secondary data from the World Bank databases and other reliable sources, in addition to satellite imageries, to classify and describe the landscape types. Data about the proportions of the landscape elements especially the built-up areas were collected and compiled from a literature review that included the relevant research articles. These data were used to train a specific CNN model that was intended to identify landscapes according to their ecological and structural properties. The derived Landscape Ecology Triangle offers a conceptual model that not only categorizes different types of landscapes but also quantifies the proportion of various landscape components within each type, thereby offering a better understanding of the landscape processes. When applied to the classification of landscape types, the approach can give an understanding of the best places for living based on ecological conditions and the suitability of the environment, thus promoting further ecological studies and better practices of land use in Bangladesh. Jahangirnagar University 2025-06-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Conference Proceedings Submission application/pdf http://journals.juniv.edu/index.php/tjr/article/view/211 10.59185/tjr.v49i1.211 The Jahangirnagar Review; Vol. 49 No. 1 (2025); 186-211 1682-7422 10.59185/tjr.v49i1 eng http://journals.juniv.edu/index.php/tjr/article/view/211/158 Copyright (c) 2025 The Jahangirnagar Review |
| spellingShingle | Landscape Ecology Triangle Landscape Types Landscape Elements CNN Model Suitability of the Environment Deep Learning in Ecology Tazrian, Abu Tanvir Mohammad Meherin, Samira Toma, Tania Islam Mahmud, Khandakar Hasan Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle |
| title | Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle |
| title_full | Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle |
| title_fullStr | Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle |
| title_full_unstemmed | Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle |
| title_short | Python-based Convolutional Neural Network Analysis of Satellite Imageries to Develop a Landscape Ecology Triangle |
| title_sort | python based convolutional neural network analysis of satellite imageries to develop a landscape ecology triangle |
| topic | Landscape Ecology Triangle Landscape Types Landscape Elements CNN Model Suitability of the Environment Deep Learning in Ecology |
| topic_facet | Landscape Ecology Triangle Landscape Types Landscape Elements CNN Model Suitability of the Environment Deep Learning in Ecology |
| url | http://journals.juniv.edu/index.php/tjr/article/view/211 |
| work_keys_str_mv | AT tazrianabutanvirmohammad pythonbasedconvolutionalneuralnetworkanalysisofsatelliteimageriestodevelopalandscapeecologytriangle AT meherinsamira pythonbasedconvolutionalneuralnetworkanalysisofsatelliteimageriestodevelopalandscapeecologytriangle AT tomataniaislam pythonbasedconvolutionalneuralnetworkanalysisofsatelliteimageriestodevelopalandscapeecologytriangle AT mahmudkhandakarhasan pythonbasedconvolutionalneuralnetworkanalysisofsatelliteimageriestodevelopalandscapeecologytriangle |