Magmatic intrusions play a vital role not only in accommodating extensional stresses in continental rifts but also in feeding volcanic systems. The location, orientation, and timescale of dike intrusions are dictated by the interaction of regional and local stresses, the effect of pre‐existing weaknesses, and the composition of magma. Observing active intrusions can provide important information regarding the interaction between magmatic processes and the tectonic stress field during continental rifting. We focus on a seismic swarm that occurred in 2015 to the northeast of Fentale volcano, in the Main Ethiopian Rift (MER), and use radar interferometry to study surface deformation associated with the seismic swarm. Interferograms show a pattern of dike‐induced deformation, with a model estimate of volume change of 33×10⁶±0.6×10⁶m³ at a depth range of 5.4 to 8 km. We use a small baseline subset algorithm to calculate line of sight time series and find that the displacements decay exponentially with a decay constant of ∼83 days. Coupled source‐sink models suggest that such slow dike intrusions require a high viscosity rhyolitic magma. The difference in behavior between Fentale and other caldera systems in the MER, which show multi‐year cycles of inflation and deflation, suggests fundamental differences in magma composition and architecture of the plumbing system. This is the first direct observation of a dike intrusion in the MER and provides new constraints on the temporal‐spatial patterns of stress and strain that occur during continental rifting. Whether this activity is transient or a long‐term feature associated with rift evolution is an open question.

Cities are the growth engines responsible for shaping the global economy, major contributors to climate change, and are significantly affected by it. However, the United Nations adopted the Sustainable Development Goals (SDGs) to make these cities and human settlements inclusive, safe, resilient, and sustainable. Yet, the rapid and unplanned urban expansion exacerbates various environmental challenges and reduces green cover in urban areas. To address these issues and meet the SDGs, stakeholders need to emphasise and optimise urban spaces. This study investigates the borough-level analysis of green spaces and human exposure to green spaces across London using satellite-derived datasets on vegetation and socio-economic factors to examine the variations in urban vegetation cover and urban population exposure to vegetation cover between 2017 and 2024. This study highlights the spatial disparity in green space coverage and exposure to green space between the inner and outer boroughs of London. The methodology used here suggests an average loss of approximately 11 and 9 percent in green space coverage and green space exposure to population, respectively, between 2017 and 2024 across London boroughs.

Urban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires effective planning, maintenance, and continuous monitoring. To enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations. Publicly available large-scale data, combined with machine learning models, can improve our understanding. We explore the potential of Sentinel-2 to classify and extract meaningful features from urban landscapes. Using advanced machine learning techniques, we aim to develop a robust and scalable framework for classifying urban environments. The proposed models will assist in monitoring changes in green spaces across diverse urban settings, enabling timely and informed decisions to foster sustainable urban growth.

Coastal heritage assets are crucial to a society’s history and must be preserved sustainably, despite their vulnerability to both natural and anthropogenic hazards. Their monitoring is challenging due to the interrelated nature of these attributes. While expert observations and on-site measurements are employed, they cover limited areas over time, whereas remote sensing can assess larger regions more regularly. This study examines the impacts of climate change on Old Town North, a conservation area within Southampton Harbour, UK, designated as “heritage at risk” by Historic England in 2024. Particular focus is given to temperature and moisture variations, which may accelerate material decay and heighten risks. Using a multidisciplinary approach, the study uses satellite data to extract land surface temperatures, monitor coastal changes, and identify vulnerable risk zones. Results show that the conservation area faces multiple pressures, including moisture deficiency, urban sprawl, and increased surface temperatures, that together could hasten the deterioration of heritage assets.

Unmanned Aerial Vehicles (UAVs) have become vital tools for environmental monitoring and disaster response, particularly in remote regions where ground-based infrastructure is sparse (Erdelj et al., 2017). Yet the practical value of these platforms depends heavily on precise flight control when navigating challenging terrain or conducting time-sensitive surveys after natural hazards. Tuning Model Predictive Control (MPC) weight matrices for such varied operational demands remains tedious and expertise-intensive, which slows deployment during crises.We present an adaptive control framework merging reinforcement learning with formal stability guarantees. A learning agent tunes controller gains online while Lyapunov-based bounds confine every candidate gain to a provably stable region (Christofides et al., 2011). A projection operator acts as a hard safety layer, clipping any out-of-bounds gain before it reaches the MPC solver. The resulting architecture preserves guaranteed stability regardless of policy network behaviour—essential when aircraft operate beyond visual line of sight in poorly monitored areas.Validation spans four UAV platforms covering a 200-fold mass range (27 g to 5.5 kg). On an aggressive 3D figure-8 trajectory (±4.0 m on both horizontal axes), tracking improves by 22–27 %. Position root mean square error falls from 0.45–0.55 m to 0.33–0.43 m, with variance reductions of 28–33 %. Across 60 evaluation trials, no stability violations occurred. Sequential transfer learning cuts per-platform training by 75 %, valuable when field crews must swap vehicles mid-campaign—switching, for instance, from a compact quadrotor on initial reconnaissance to a heavier hexacopter carrying hyperspectral sensors.These results show that rigorous stability guarantees and learning-based adaptation can coexist. For observation campaigns in areas lacking ground-based networks, self-tuning controllers that never risk unstable flight could meaningfully extend what small drone fleets achieve—whether assessing earthquake damage or inspecting infrastructure in informal settlements. ReferencesChristofides, P.D., Liu, J., Muñoz de la Peña, D. (2011). Lyapunov-Based Model Predictive Control. In: Networked and Distributed Predictive Control. Advances in Industrial Control. Springer, London.Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Computing, 16(1), 24–32.