Cloudy and rainy environments are common in tropical, subtropical, and other regions frequently affected by cloud cover and rainfall. These challenging conditions pose significant obstacles to remote sensing data acquisition, processing, and applications, while also motivating innovation in methodologies and technologies.

Photorealistic scene reconstruction from 2D images is a long-standing, fundamental research area that spans computer vision, graphics, and photogrammetry. Many applications from this research, e.g., in autonomous navigation, augmented reality, smart cities, disaster relief planning, etc., can greatly benefit society.

Remote sensing is witnessing an unprecedented data revolution, marked by the explosive growth of heterogeneous Earth Observation (EO) data from diverse sensors—optical, SAR, LiDAR, hyperspectral, and thermal—across multiple resolutions.

Accurate 3D perception in underwater environments has become a critical requirement for various ocean engineering tasks such as subsea infrastructure inspection, autonomous navigation of underwater vehicles, habitat mapping, and environmental monitoring.

Hyperspectral imaging has emerged as a transformative technology in Earth observation, capturing detailed spectral information across hundreds of narrow, contiguous bands. This rich spectral data enables precise material identification and quantitative analysis, supporting applications in agriculture, environmental monitoring, mineral exploration, and urban planning.

Over the past decade or so, artificial intelligence technologies represented by deep learning have made remarkable progress, especially in the domains of signal processing and computer vision. In these domains, deep learning-based methods are being iterated and commercialized at an unprecedented rate, dramatically changing the way humans live, learn, and work.

The rapid growth of video content across various domains has led to an increasing demand for more intelligent and efficient video understanding and assessment techniques. This Special Issue focuses on the integration of multimodal information, such as audio, text, and sensor data, with video to enhance processing, analysis, and interpretation.

Geospatial image analysis has emerged as a cornerstone for mission-critical applications across diverse domains, ranging from urban modeling (e.g., multi-sensor UAV navigation in degraded visibility) to dynamic environmental monitoring (e.g., near-real-time disaster response), and the rising paradigm of foundation model-driven scene interpretation.

It has been over a decade since super-resolution microscopy was awarded the 2014 Nobel Prize in Chemistry, and after tremendous advancements over the years, it is on its way to becoming a standard biomedical research tool. Super-resolution imaging has redefined the boundaries of what is optically visible, unlocking previously inaccessible insights into the nanoscale