Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators

BACKGROUND: The rapid and often uncontrolled rural-urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa's population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. METHODS: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2-10 years (PfPR2-10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2-10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. RESULTS: The results suggest that the spatial distribution of PfPR2-10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2-10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. CONCLUSIONS: The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Need for an integrated deprived area "slum" mapping system (IDEAMAPS) in low-and middle-income countries (LMICS)

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying "slum households" in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low-and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas

Long-term mapping of urban areas using remote sensing: Application of deep learning using case-studies of data from Central Africa

Urbanisation has had a profound impact in sub-Saharan Africa and can be attributed to the complex human-environment interaction. Knowledge on long-term urbanisation processes in sub-Saharan Africa is lacking. Besides, understanding the urbanisation process in sub-Saharan Africa necessitates to go beyond the global scale to the local scale to unravel the idiosyncrasies in the growth of each city. The perennial lack of data (or data meeting required specifications) is a bottleneck to such studies. Very often, the nature of the available data might present challenges to existing classification algorithms. Moreover, availability of adequate and well-curated reference datasets presents a bottleneck to the generation of accurate land-cover maps. The main aim of this research was to conduct a long-term analysis of urbanisation patterns in Central Africa by developing methodologies based on deep learning, a class of Artificial Intelligence. To this end, we address the main research question: “How can we understand long-term urbanisation patterns by applying deep learning on digital aerial images in Central Africa?” We used case-studies from three cities in Central Africa namely Goma and Bukavu in The Democratic Republic of Congo and Bujumbura in Burundi, to aid in understanding the urbanisation process in sub-Saharan Africa. We generate baseline data from an archive of historical panchromatic orthomosaics that allow for the capturing of the urbanisation before the onset of rapid urbanisation that was characteristic of Countries in sub-Saharan Africa after gaining independence from colonialism.The main contribution of this thesis is the 60-year long-term analysis of urbanisation patterns for three cities in Central Africa using a unique dataset of historical orthomosaics. The growth patterns and driving forces are analysed using spatial and qualitative data. The results show that social triggers such as wars drive urban expansion. On the contrary, biophysical drivers such as geohazards did not limit urban growth only slowing down settlements for a short time span of the analysis. As urbanisation levels increased, constraining effects of natural environment such as relief on urban expansion weakened. In addition, we make some methodological developments based on deep learning to generate land-cover from historical panchromatic orthomosaics (i.e. 1m spatial resolution) and sub-metric RGB aerial images (i.e. 0.175m). Results show that deep learning methods generally have high accuracy metrics, compared to standard machine learning baselines, but at the cost of high demand for a large and accurate, labelled dataset and computational resources. In addition, accurate and sufficient labelled data are still needed to guarantee accurate land-cover maps from deep learning algorithms and novel strategies need to be pursued in approaches investigating insufficient reference data.L’urbanisation a eu un impact profond sur l’Afrique subsaharienne et peut être attribuée à l’interaction complexe entre l’homme et l’environnement. Les connaissances sur les processus d’urbanisation sur des temps longs font défaut. En outre, pour comprendre le processus d'urbanisation en Afrique subsaharienne, il faut aller au-delà de l'échelle mondiale et s'intéresser à l'échelle locale pour comprendre les particularités de la croissance de chaque ville. L'éternel manque de données (ou de données répondant aux spécifications requises) constitue des limitations de telles études. Très souvent, la nature des données disponibles peut présenter des défis pour les algorithmes de classification existants. De plus, la disponibilité d'ensembles de données de référence adéquats et fiables constitue un goulot d'étranglement pour la création de cartes précises de l'occupation des sols.L'objectif principal de cette recherche était de mener une analyse sur des temps longs des différentes modes d'urbanisation en Afrique centrale en développant des méthodologies basées sur le deep learning, une forme d'intelligence artificielle. A cette fin, nous répondons à la question de recherche suivante :"Comment pouvons-nous comprendre les modes d'urbanisation sur des temps longs en appliquant des approches de deep learning sur des images aériennes numériques en Afrique centrale ?". Nous avons étudié trois villes d'Afrique centrale, à savoir Goma et Bukavu en République démocratique du Congo et Bujumbura au Burundi, de manière à comprendre le processus d'urbanisation en Afrique subsaharienne. Nous pouvons générer des données de base à partir de mosaïques d'orthophotographies aériennes panchromatiques historiques qui permettent de saisir l'urbanisation avant le début de l'urbanisation rapide ayant caractérisé les pays d'Afrique subsaharienne depuis leur indépendance.La principale contribution de cette thèse est l'analyse, sur 60 ans, des modes d'urbanisation pour trois villes d'Afrique centrale en utilisant un ensemble unique d'orthomosaïques historiques. Les modes et les facteurs de croissance y sont analysés en utilisant des données spatiales et qualitatives. Les résultats montrent que les déclencheurs sociaux tels que les guerres étaient positivement corrélé à l'expansion urbaine. Au contraire, les facteurs biophysiques tels que les risques associés aux catastrophes naturelles n'ont pas empêché la croissance urbaine mais ont seulement ralenti les nouvelles implantations pendant une courte période de l'analyse. Par ailleurs, à mesure que les niveaux d'urbanisation augmentent, les effets contraignants de l'environnement naturel tels que le relief sur l'expansion urbaine s'affaiblissent.De plus, certaines avancées méthodologiques ont été accomplies en explorant et en développant une méthodologie basée sur le deep learning pour générer une carte de la couverture du sol à partir d’orthomosaïques panchromatiques historiques (1m) et d'images aériennes RVB sub-métriques (0.175m). Les résultats montrent que les méthodes de deep learning présentent généralement des indicateurs de précision élevés par rapport aux méthodes standard basée sur machine learning, mais au prix d'une grande demande en matière de données étiquetées et de ressources informatiques. En outre, des données étiquetées précises et en quantité suffisante sont toujours nécessaires pour garantir l'exactitude des cartes de l’occupation du sol établies par les algorithmes de deep learning, et de nouvelles stratégies doivent être mises en œuvre dans le cas de travaux ne disposant que de données de référence insuffisantesDoctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa

Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved

Detection of informal settlements from VHR satellite images using convolutional neural networks

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Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks

info:eu-repo/semantics/publishe

Weakly supervised fully convolutional networks using OBIA classification output

In this paper, we present a methodology for preparing reference data to be used in training a fully convolutional neural network. It is a laborious and time-consuming task to prepare adequate training data for a Fully convolutional network (FCN) since the tiles need to be fully labeled. Weakly supervised learning is used when there are inadequate, inaccurate or incomplete training labels. In this paper, coarse labels are prepared using visual image interpretation and used to train an existing semi-automatic Object-based image analysis (OBIA) chain and subsequently used to classify a very high-resolution aerial (VHR) imagery of the city of Goma, the Democratic Republic of Congo. After accuracy assessment, fully labeled training tiles are automatically extracted and used to train an FCN designed with a skip architecture and dilated convolutions. An overall accuracy of 90.7% is attained from the tests, which demonstrates that FCN is robust to noisy labels. Future steps will entail the evaluation of ensemble voting and class probabilities in preparing the training data. This approach is promising and can address the challenge of preparing a large amount of training data for training FCNs.info:eu-repo/semantics/publishe

Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery

Land cover Classified maps obtained from deep learning methods such as Convolutional neural networks (CNNs) and fully convolutional networks (FCNs) usually have high classification accuracy but with the detailed structures of objects lost or smoothed. In this work, we develop a methodology based on fully convolutional networks (FCN) that is trained in an end-to-end fashion using aerial RGB images only as input. Skip connections are introduced into the FCN architecture to recover high spatial details from the lower convolutional layers. The experiments are conducted on the city of Goma in the Democratic Republic of Congo. We compare the results to a state-of-the art approach based on a semi-automatic Geographic object image-based analysis (GEOBIA) processing chain. State-of-the art classification accuracies are obtained by both methods whereby FCN and the best baseline method have an overall accuracy of 91.3% and 89.5% respectively. The maps have good visual quality and the use of an FCN skip architecture minimizes the rounded edges that is characteristic of FCN maps. Additional experiments are done to refine FCN classified maps using segments obtained from GEOBIA generated at different scale and minimum segment size. High OA of up to 91.5% is achieved accompanied with an improved edge delineation in the FCN maps, and future work will involve explicitly incorporating boundary information from the GEOBIA segmentation into the FCN pipeline in an end-to-end fashion. Finally, we observe that FCN has a lower computational cost than the standard patch-based CNN approach especially at inference

Deep-learning for very-high resolution land-cover mapping: a case study using Walloon open data aerial imagery and foss4g

info:eu-repo/semantics/nonPublishe

Mapping slums and model population density using earth observation data and open source solutions

This paper presents a collection of frameworks aiming at mapping land cover, land use and estimate population densities from very-high resolution images and relying on open-source software. Using height information and landscape metrics, slums location and extent can be accurately extracted from the rest of the city. Moreover, the processing chain developed can deal with large amount of data and produce useful pieces of geographical information citywide. All the results, methods and computer code are available in open-access for anyone and any purpose.info:eu-repo/semantics/publishe