323 research outputs found
Applications des techniques d'apprentissage profond aux images satellites THR pour l'aide au recensement de population en Afrique
La connaissance de la donnée démographique est une information précieuse pour les initiatives de planification. Généralement, ce sont les opérations de recensement, d’enquête et de projection démographique qui permettent de disposer de cette information. Ces opérations, dans certains pays en développement, posent divers défis d’ordre économique et logistique, privant ainsi les autorités d'informations précises et à jour sur leur population. Dans le souci d’apporter des approches de solution à cette situation, notre étude évalue une méthode d’estimation de population basée sur la détection de géo-objets résidentiels (maisons) sur des images satellites THR à l’aide de réseau de neurones convolutifs (CNN). L’approche s’appliquerait à des pays où un recensement complet est difficile à réaliser pour des raisons de moyens ou d’instabilité politique. Ainsi, une image satellite THR de 2008 du Soudan est annotée selon 07 classes de bâtis pour construire un ensemble de données qui a servi à entrainer un modèle de détection d’objets : Faster R-CNN, par Transfert Learning. Le modèle a obtenu une précision moyenne (mAP) de 79 % en entrainement et 99 % en validation. Ce modèle a été ensuite utilisé par fine tuning pour détecter les mêmes classes de bâtis sur des images de 2021. Un lien entre les géo-objets résidentiels et la taille de population a été établi en utilisant les données de population de 2008 et les données terrain disponibles. Ceci a permis une caractérisation de la population actuelle et devra aider à la préparation du recensement de 2023. Des limites de cette approche ont été soulevées. En autre, il pourra être utilisé pour améliorer le cadre de la collecte de données de population dans les pays en développement
Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification
In this article, we present a novel hybrid framework, which integrates spatial–temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial–temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial–temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe identification and monitoring of informal settlements in urban areas is an important
step in developing and implementing pro-poor urban policies. Understanding when,
where and who lives inside informal settlements is critical to efforts to improve their
resilience. This study aims at integrating OSM data and sentinel-2 imagery for
classifying and monitoring the growth of informal settlements methods to map informal
areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth
in Kampala. Three building feature characteristics of size, shape and Distance to nearest
Neighbour were derived and used to cluster and classify informal areas using Hotspot
Cluster analysis and ML approach on OSM buildings data. The resultant informal
regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal
changes in informal areas using Convolutional Neural Networks (CNNs).
Results from Optimized Hot Spot Analysis and Random Forest Classification show that
Informal regions can be mapped based on building outline characteristics. An accuracy
of 90.3% was achieved when an optimally trained CNN was executed on a test set of
2019 satellite image tiles. Predictions of informality from new datasets for the years
2016 and 2017 provided promising results on combining different open source
geospatial datasets to identify, classify and monitor informal settlements
Few-shot learning for post-earthquake urban damage detection
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAmong natural disasters, earthquakes are recorded to have the highest rates in human
loss in the past 20 years. Their unexpected nature has severe consequences on
both human lives and material infrastructure and demands urgent action. For effective
emergency relief, it is necessary to gain awareness about the level of damage in the
affected areas. The use of remotely sensed imagery is popular in damage assessment
applications, however it requires a considerable amount of labeled data, which are
not always easy to obtain. Taking into consideration the recent developments in the
fields of Machine Learning and Computer Vision, this thesis investigates and employs
several Few-Shot Learning (FSL) strategies in order to address data insufficiency and
imbalance in post-earthquake urban damage classification. The contribution of this
work is double: we manage to prove that oversampling is the most suitable data balancing
method for training Deep Convolutional Neural Networks (CNN) when compared
to cost-sensitive learning and undersampling, and to demonstrate the feasibility
of Prototypical Networks in a damage classification problem
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
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