## 4 Challenges and Future Directions  
Sinkholes, caused by natural or anthropogenic processes, require high-resolution monitoring for accurate characterization and risk assessment. Techniques like TLS and ground-based radar enable 3D mapping of sinkhole-prone areas with sub-centimeter precision, while 4D monitoring tracks temporal changes through repeated scans. Integrating subsurface imaging methods, such as GPR and ERT, creates comprehensive 3D models. Data processing involves noise reduction, reconstruction of digital elevation models, and time series analysis to identify precursors to sinkhole formation. Challenges include high data volumes, environmental factors, and integrating datasets from diverse sources. Future advancements in AI, sensor fusion, and edge computing will streamline data processing, improve resolution, and enable real-time hazard detection. Emerging techniques like distributed acoustic sensing and probabilistic models will further enhance capabilities. Subsidence and landslides related to continuous base-level drop and ongoing adaptation of underground water flows pose additional challenges. These phenomena are exacerbated by fluctuating water tables, sediment redistribution, and karstification, leading to surface instability. Continuous monitoring using InSAR, combined with hydrological modeling, is essential to understand these processes and their interactions with geological and human-induced activities. By identifying regions experiencing rapid changes, mitigation strategies can be designed to reduce risks. Economic and logistical challenges, particularly in hazardous or remote areas, underscore the importance of collaborative data-sharing platforms and open-access satellite data. Policy-level engagement and international partnerships will promote the widespread adoption and integration of these advanced technologies, fostering resilience to geological and environmental changes. Future directions involve leveraging new satellite missions, such as Sentinel-3, Sentinel-4, Landsat 9, WorldView, and Pleiades Neo, which provide higher spatial and temporal resolutions, improved data coverage, and enhanced spectral imaging. Additionally, constellations of small satellites from private companies like PlanetScope (US), Umbra (US), Capella (US), and Iceye (Finland) significantly shorten revisit periods while enabling the use of a wide range of electromagnetic waves to image the ground. These advancements are crucial for 4D monitoring, as they allow detailed tracking of surface deformation and environmental anomalies over time. Combining these datasets with methodologies like InSAR and ML facilitates a more precise analysis of geological processes, including ground subsidence and tectonic shifts. Ground-based techniques, such as LiDAR, GPR, and TLS, can complement satellite data for increased accuracy and reliability. Advancements in remote sensing, particularly InSAR and machine learning, offer promising solutions for sinkhole detection and hazard assessment. Among these, the Sinkhole Scanner [60] stands out for its ability to identify sinkhole-related spatiotemporal deformation patterns in InSAR time series data. Using a mathematical framework such as an inverted Gaussian function applied within a moving window, it calculates posterior variances to pinpoint subsiding zones indicative of sinkhole activity. The Scanner has proven effective across varying spatial scales, including Sentinel-1 imagery over Ireland, detecting precursory deformation zones with precision and enabling early hazard warnings. Challenges remain, including noise introduced by hyper-salinity, fluctuating water levels, and active tectonics. The Scanner's effectiveness relies on high CCS density, which may be limited in rural or unstable areas. Additionally, the temporal resolution of available satellite datasets can constrain the capture of rapid deformation processes. Addressing these limitations, integrating the Scanner with advanced ML frameworks like LSTMs and CNNs [27] enhances its scalability and precision.