During Sudan's civil war, which erupted in April 2023, both sides have increasingly relied on drones, and civilians have borne the brunt of the carnage. The conflict between the Sudanese armed forces (SAF) and the Rapid Support Forces (RSF) paramilitary group is an example of war transformed by commercially available, easily concealable unmanned aerial vehicles (UAVs), or drones. Modular, well-adapted to sanctions evasions and devastatingly effective, drones have killed scores of civilians, crippled infrastructure and plunged Sudanese cities into darkness. In this visual investigation, Al Jazeera examines the history of drone warfare in Sudan, the types of drones used by the warring sides, how they are sourced, where the attacks have occurred and the human toll. The RSF traces its origins to what at the time was a government-linked militia known as the Janjaweed.

Sudanese refugee children watch the sunset in the Tine transit camp amid the conflict between the paramilitary Rapid Support Forces (RSF) and the Sudanese Army, in eastern Chad on Nov. 23. Port Sudan, Sudan - A recent paramilitary drone attack on the army-held town of Kalogi in Sudan's South Kordofan state hit a kindergarten and a hospital, killing dozens of civilians including children, a local official said Sunday. The attack, which took place on Thursday, involved three strikes, first a kindergarten, then a hospital and a third time as people tried to rescue the children, Essam al-Din al-Sayed, head of the Kalogi administrative unit, said using a Starlink satellite internet connection. He blamed the assault on the Rapid Support Forces and their ally, the Sudan People's Liberation Movement-North faction (SPLM-N) led by Abdelaziz al-Hilu, which controls much of South Kordofan and parts of Blue Nile state. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models. We pair building footprint and height data from existing datasets with quarterly PlanetScope basemap satellite images to train a multi-task deep learning model that predicts building density and building height at a 37.6-meter per pixel resolution. We apply this model to global PlanetScope basemaps from Q1 2018 through Q2 2025 to create global, temporal maps of building density and height. We validate these maps by comparing against existing building footprint datasets. Our estimates achieve an F1 score between 85% and 88% on different hand-labeled subsets, and are temporally stable, with a 0.96 five-year trend-consistency score. TEMPO captures quarterly changes in built settlements at a fraction of the computational cost of comparable approaches, unlocking large-scale monitoring of development patterns and climate impacts essential for global resilience and adaptation efforts.