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Building identification in aerial imagery using deep learning
Thesis (MSc)--Stellenbosch University, 2024.
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| Format: | Thesis |
| Language: | en_ZA en_ZA |
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Stellenbosch : Stellenbosch University
2024
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| _version_ | 1867613923598925824 |
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| access_status_str | Open Access |
| author | Nakiranda, Proscovia |
| author2 | Grobler, Trienko |
| author_browse | Grobler, Trienko Nakiranda, Proscovia |
| author_facet | Grobler, Trienko Nakiranda, Proscovia |
| author_sort | Nakiranda, Proscovia |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/130619 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA en_ZA |
| last_indexed | 2026-06-10T12:43:51.865Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/130619 Building identification in aerial imagery using deep learning Nakiranda, Proscovia Grobler, Trienko Stellenbosch University. Faculty of Science. Dept. of Computer Science. Image segmentation Remote-sensing images -- Data processing Image processing -- Digital techniques Deep learning (Machine learning) -- Technological innovations Neural networks (Computer science) Aerial photography -- Computer network resources UCTD Thesis (MSc)--Stellenbosch University, 2024. ENGLISH ABSTRACT: Advancements in the field of remote sensing have facilitated the effortless re- trieval of information about any location on Earth at any given time. This has resulted in a variety of developments, including the identification of economic activities taking place in a particular area. The task of identifying build- ings is one significant application of remote sensing imagery as it is crucial for assessing resource distribution, especially in low-resource or data-scarce areas. Advances in machine learning and computation resources allow for au- tomatic analysis of collected remote sensing data, eliminating the need for human intervention. The task of building identification falls under computer vision and is an example of a task that can be automated. Several machine learning architectures (models) have been proposed for building identification. However, choosing the appropriate one can be challenging due to limitations such as difficulty in accurately identifying boundary or near boundary pixels, resource requirements, and overall model accuracy. Therefore, conducting a comparative study is necessary to evaluate the performance of the building identification models. In this thesis, we carry out a comparative study of four state-of-the-art models used for the building identification task. We eval- uate their performance both qualitatively and quantitatively. Furthermore, we investigate the effect of multitask learning on the models’ performance in building identification. The thesis concludes by providing our research find- ings and outlining prospective future research avenues. Moreover, it provides a thorough overview of the fundamental theory underpinning remote sensing and machine learning. AFRIKAANSE OPSOMMING: Vooruitgang in die veld van afstandaardobservasie het dit moontlik gemaak om moeiteloos inligting van enige plek op aarde te bekom. Dit het ook tot verskeie ontwikkelings gelei, insluitende die vermoë om die ekonomiese aktiwiteite wat plaasvind in ’n gegewe area te identifiseer. Die taak om geboue te idetifiseer is een van die mees belangrikste toepassings in afstandaardobservasie omdat dit krities is vir effektiewe hulpbronbeplanning. Dit is veral belangrik vir ge- biede waar daar beperkte hulpbronne is. Vooruitgang in masjienleer en harde- ware maak dit moontlik om sekere take te automatiseer. Die taak van gebou- identifiseering is so ’n toepassing wat onder die veld van rekenaarvisie val. Ver- skeie masjienleer argitekture (modelle) word gebruik vir hierdie taak. Om die beste model te kies vir ’n spesifieke gebruiksgeval is nie altyd maklik nie, omdat verskillende dinge in ag geneem moet word wanneer so ’n besluit gemaak moet word. Dinge soos hoe akkuraat kan randpieksels onderskei word, beskikbare verwerkingskrag en die alghele akkuraatheid van modele moet in ag geneem word. Dit is hoekom vergelykingstudies van gebou-identifiseeringsargitekture van kardinale belang is. In hierdie tesis, word ’n vergelykingstudie tussen vier algeheel gebruikte gebou-identifiseeringsargitekture uitgevoer. Hierdie studie is beide kwantitatief en kwalitatief. Verder word daar ook ondersoek watter effek veeltaak afrigting het op gebou-identifiseeringsargitekture. Die tesis sluit af met die bevindinge van die studie en daar word voorstelle gemaak oor wat in die toekoms gedoen kan word. Die tesis gee ook ’n goeie oorsig van die volgende velde: masjienleer en afstandaardobservasie. Masters 2024-02-09T12:42:07Z 2024-04-27T00:12:21Z 2024-02-09T12:42:07Z 2024-04-27T00:12:21Z 2024-03 Thesis https://scholar.sun.ac.za/handle/10019.1/130619 en_ZA en_ZA Stellenbosch University xvi, 109 pages : illustrations (some color) application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Image segmentation Remote-sensing images -- Data processing Image processing -- Digital techniques Deep learning (Machine learning) -- Technological innovations Neural networks (Computer science) Aerial photography -- Computer network resources UCTD Nakiranda, Proscovia Building identification in aerial imagery using deep learning |
| title | Building identification in aerial imagery using deep learning |
| title_full | Building identification in aerial imagery using deep learning |
| title_fullStr | Building identification in aerial imagery using deep learning |
| title_full_unstemmed | Building identification in aerial imagery using deep learning |
| title_short | Building identification in aerial imagery using deep learning |
| title_sort | building identification in aerial imagery using deep learning |
| topic | Image segmentation Remote-sensing images -- Data processing Image processing -- Digital techniques Deep learning (Machine learning) -- Technological innovations Neural networks (Computer science) Aerial photography -- Computer network resources UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/130619 |
| work_keys_str_mv | AT nakirandaproscovia buildingidentificationinaerialimageryusingdeeplearning |