Group-privacy threats for geodata in the humanitarian context

The role of geodata technologies in humanitarian action is arguably indispensable in determining when, where, and who needs aid before, during, and after a disaster. However, despite the advantages of using geodata technologies in humanitarianism (i.e., fast and efficient aid distribution), several ethical challenges arise, including privacy. The focus has been on individual privacy; however, in this article, we focus on group privacy, a debate that has recently gained attention. We approach privacy through the lens of informational harms that undermine the autonomy of groups and control of knowledge over them. Using demographically identifiable information (DII) as a definition for groups, we first assess how these are derived from geodata types used in humanitarian DRRM. Second, we discuss four informational-harm threat models: (i) biases from missing/underrepresented categories, (ii) the mosaic effect—unintentional sensitive knowledge discovery from combining disparate datasets, (iii) misuse of data (whether it is shared or not); and (iv) cost–benefit analysis (cost of protection vs. risk of misuse). Lastly, borrowing from triage in emergency medicine, we propose a geodata triage process as a possible method for practitioners to identify, prioritize, and mitigate these four group-privacy harms

Context-Based Filtering of Noisy Labels for Automatic Basemap Updating From UAV Data

Unmanned aerial vehicles (UAVs) have the potential to obtain high-resolution aerial imagery at frequent intervals, making them a valuable tool for urban planners who require up-to-date basemaps. Supervised classification methods can be exploited to translate the UAV data into such basemaps. However, these methods require labeled training samples, the collection of which may be complex and time consuming. Existing spatial datasets can be exploited to provide the training labels, but these often contain errors due to differences in the date or resolution of the dataset from which these outdated labels were obtained. In this paper, we propose an approach for updating basemaps using global and local contextual cues to automatically remove unreliable samples from the training set, and thereby, improve the classification accuracy. Using UAV datasets over Kigali, Rwanda, and Dar es Salaam, Tanzania, we demonstrate how the amount of mislabeled training samples can be reduced by 44.1% and 35.5%, respectively, leading to a classification accuracy of 92.1% in Kigali and 91.3% in Dar es Salaam. To achieve the same accuracy in Dar es Salaam, between 50000 and 60000 manually labeled image segments would be needed. This demonstrates that the proposed approach of using outdated spatial data to provide labels and iteratively removing unreliable samples is a viable method for obtaining high classification accuracies while reducing the costly step of acquiring labeled training samples

Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge

The digital transformation taking place in all areas of life has led to a massive increase in digital data – in particular, related to the places where and the ways how we live. To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multi-source data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP’s pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development

Explainable AI for earth observation: A review including societal and regulatory perspectives

Artificial intelligence and machine learning are ubiquitous in the domain of Earth Observation (EO) and Remote Sensing. Congruent to their success in the domain of computer vision, they have proven to obtain high accuracies for EO applications. Yet experts of EO should also consider the weaknesses of complex, machine-learning models before adopting them for specific applications. One such weakness is the lack of explainability of complex deep learning models. This paper reviews published examples of explainable ML or explainable AI in the field of Earth Observation. Explainability methods are classified as: intrinsic versus post-hoc, model-specific versus model-agnostic, and global versus local explanations and examples of each type are provided. This paper also identifies key explainability requirements identified the social sciences and upcoming regulatory recommendations from UNESCO Ethics of Artificial Intelligence and requirements from the EU draft Artificial Intelligence Act and analyzes whether these limitations are sufficiently addressed in the field of EO. The findings indicate that there is a lack of clarity regarding which models can be considered interpretable or not. EO applications often utilize Random Forests as an “interpretable” benchmark algorithm to compare to complex deep-learning models even though social sciences clearly argue that large Random Forests cannot be considered as such. Secondly, most explanations target domain experts and not possible users of the algorithm, regulatory bodies, or those who might be affected by an algorithm’s decisions. Finally, publications tend to simply provide explanations without testing the usefulness of the explanation by the intended audience. In light of these societal and regulatory considerations, a framework is provided to guide the selection of an appropriate machine learning algorithm based on the availability of simpler algorithms with a high predictive accuracy as well as the purpose and intended audience of the explanation