The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

The majority of urban inhabitants in low-and middle-income country (LMIC) cities live in
deprived urban areas. However, policy efforts and the monitoring of global goals and …

Mapping deforestation is an essential step in the process of managing tropical rainforests. It
lets us understand and monitor both legal and illegal deforestation and its implications …

Deep learning (DL) algorithms have recently emerged from machine learning and soft
computing techniques. Since then, several deep learning (DL) algorithms have been …

Many cities in low-and medium-income countries (LMICs) are facing rapid unplanned
growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is …

From the perspective of historical review, the methodology of economics develops from
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …

Along with rapid urbanization, the growth and persistence of slums is a global challenge.
While remote sensing imagery is increasingly used for producing slum maps, only a few …

Earth observation (EO) capabilities to produce up-to-date geographical information on slums
over large areas supporting urban planning and evidence-based policymaking are largely …

Boundaries of agricultural fields are important features necessary for defining the location,
shape, and spatial extent of agricultural units. They are commonly used to summarize …

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 …