The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

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

Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context

Background and objectives: The development of mobile and on the edge appli-cations that embed deep convolutional neural models has the potential to revolutionisebiomedicine. However, most deep learning models require computational resourcesthat are not available in smartphones or edge devices; an issue that can be faced bymeans of compact models. The problem with such models is that they are, at leastusually, less accurate than bigger models. In this work, we study how this limitationcan be addressed with the application of semi-supervised learning techniques.Methods: We conduct several statistical analyses to compare performance of deepcompact architectures when trained using semi-supervised learning methods for tack-ling image classification tasks in the biomedical context. In particular, we explore threefamilies of compact networks, and two families of semi-supervised learning techniquesfor 10 biomedical tasks.Results: By combining semi-supervised learning methods with compact net-works, it is possible to obtain a similar performance to standard size networks. Ingeneral, the best results are obtained when combining data distillation with MixNet,and plain distillation with ResNet-18. Also, in general, NAS networks obtain betterresults than manually designed networks and quantized networks.Conclusions: The work presented in this paper shows the benefits of apply semi-supervised methods to compact networks; this allow us to create compact models thatare not only as accurate as standard size models, but also faster and lighter. Finally,we have developed a library that simplifies the construction of compact models usingsemi-supervised learning method