Suchergebnisse

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. ; SCOPUS: ar.j ; info:eu-repo/semantics/published

Remote sensing images provide the capability to obtain information of various landcover classes. This information is useful to the process of urban planning, socio-economicmodelling and population studies. There is a need for accurate and efficient methods to obtainthis information, particularly in regions where there is a scarcity of reference data. In thiswork, we develop a methodology based on fully convolutional networks (FCN) that is trainedin an end-to-end fashion using aerial RGB images only as input. The experiments are conductedon 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 bothmethods whereby FCN and the best baseline method have an overall accuracy of 91.24% and89.34% respectively. The maps have good visual quality and the use of a FCN skip architectureminimizes the rounded edges that is characteristic of FCN maps. Finally, additionalexperiments are done to refine FCN classified maps using segments obtained from GEOBIA.This resulted in improved edge delineation in the FCN maps, and future work will involveexplicitly incorporating boundary information from the GEOBIA segmentation into the FCNpipeline in an end-to-end fashion. ; info:eu-repo/semantics/published

Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e. from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urgently required for the production of EO-based mapping products. Combining an online survey and several forms of user interactions, we defined five system specifications (derived from user requirements) for designing an open-access spatial information system for deprived urban areas. First, gridded maps represent the optimal spatial granularity to deal with high uncertainties of boundaries of deprived areas and to protect privacy. Second, a high temporal granularity of 1–2 years is important to respond to the high spatial dynamics of urban areas. Third, detailed local-scale information should be part of a city-to-global information system. Fourth, both aspects, community assets and risks, need to be part of an information system, and such data need to be combined with local community-based information. Fifth, in particular, civil society and government users should have fair access to data that bridges the digital barriers. A data ecosystem on urban deprivation meeting these requirements will be able to support community-level action for improving living conditions in deprived areas, local science-based policymaking, and tracking progress towards global targets such as the SDGs. ; info:eu-repo/semantics/published

In the framework of Mine Action, the extent of Suspected Hazardous Areas (SHAs) is often overestimated. This study investigates the potential of Object‐Based Image Analysis (OBIA) for extracting Indicators of Mine Presence (IMP) to support a more precise delineation of SHAs, with the aim of ensuring an optimal use of demining resources. The study area is situated in the Svilaja mountain range in Croatia. Using 3K colour aerial photographs, we implemented two approaches for the extraction of dry stone walls located in an area that displays traces of military activities. The first approach uses object‐based class modelling, which describes an iterative process of segmentation and classification. The second approach implements supervised learning techniques based on advanced statistical classification methods, i.e. Support Vector Machines, Random Forests and Recursive Partitioning. The results are compared, the strengths and limitations of both approaches are discussed, and perspectives for further improvements are considered. ; info:eu-repo/semantics/published

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11 - Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.