Digital Earth applications provide a common ground for visualizing, simulating, and modeling real-world situations. The potential of Digital Earth applications has increased significantly with the evolution of artificial intelligence systems and the capacity to collect and process complex amounts of geospatial data. Yet, the widespread techno-optimism at the root of Digital Earth must now confront concerns over high-risk artificial intelligence systems and power asymmetries of a datafied society. In this commentary, we claim that not only can current debates about data governance and ethical artificial intelligence inform development in the field of Digital Earth, but that the specificities of geospatial data, together with the expectations surrounding Digital Earth applications, offer a fruitful lens through which to examine current debates on data governance and artificial intelligence ethics. In particular, we argue that for the implementation of ethical artificial intelligence and inclusive approaches to data governance, Digital Earth initiatives need to involve stakeholders and communities at the local level and be sensitive to social, legal, cultural, and institutional contexts, including conflicts that might arise within those contexts.

Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity.A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution.The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.

The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data. All data is in scope, whether born digital or converted from other sources.

Background: This paper explores strategies to enhance North-South collaboration to foster multi- and trans-disciplinary research in disaster risk reduction (DRR) and crisis management, emphasizing the role of open science, Artificial Intelligence (AI) and geoinformatics in developing a resilient and proactive approach to these challenges, as well as drawing on lessons learned from the COVID-19 pandemic, the paper highlights how collaboration and data sharing can enhance preparedness, response, and recovery in future crises. Methods: The study uses a mixed approach to gather and analyze information. Qualitative methods focus on gathering descriptive data through engaging with experts and a structured review. By investigating literature, a comprehensive strategic framework for minimizing disaster risk, and for crisis management, is developed to provide adequate guidance to policymakers on implementation strategies and the application of how to build the human, institutional, and technological capacity required for effective disaster and crisis management (DCM). The events were used to engage with experts to provide recommendations and opportunities to enhance crisis response, capacity development and policy. Findings:>By presenting a comprehensive framework that bridges theoretical knowledge with practical application, our findings indicate that governments must address the challenges of DCM to minimize losses. The practices surrounding disaster recovery can provide opportunities for policymakers to reassess priorities and redirect resources toward building sustainable communities. Originality: There is no strategic framework that provides adequate guidance to policymakers on implementation strategies or the application of how to build the human, institutional and technological capacity required for effective DCM. This article develops a Decision Support Framework for DCM. We outline how open science promotes cross-disciplinary collaboration and the sharing of knowledge, while geoinformatics, AI enable real-time data analysis that supports proactive disaster preparedness, response, and recovery. This paper encompasses a strategic, comprehensive, capacity-building framework for achieving sustainable strengthening of North-South collaboration. Furthermore, it presents how to design the most efficient data policies in times of crisis and how we can use these tools to improve North-South cooperation in Africa’s DCM. Finally, it highlights challenges of implementation and presents solutions and opportunities for Africa.