Forest change detection plays a vital role in remote sensing research, serving as a cornerstone for ecological protection and sustainable environmental management. While existing research on change detection primarily focuses on urban areas and croplands, forest change detection remains underdeveloped. As the demand for real-time monitoring of forest changes grows in response to challenges like climate change and environmental degradation, advancements in remote sensing technologies make it possible to bridge this research gap. The complexity of forest change detection stems from high seasonal and inter-annual variability, with fluctuations often showing strong similarities across years. In this paper, we introduce FCDNet:A Multi-Scale Attention Networkfor Forest Change DetectionUsing Dual-Temporal Very-High-Resolution Remote Sensing Images. The proposed network is designed to enhance the precision and robustness of change detection through the integration of multiscale feature aggregation and an adaptive channel attention strategy. This design enables FCDNet to sensitively identify fine-grained forest alterations while maintaining strong adaptability to spatial and temporal variability in the input imagery. Comprehensive experiments confirm that FCDNet delivers superior performance compared with existing state-of-the-art approaches, showing notable advantages under challenging scenarios characterized by seasonal transitions and inter-annual fluctuations. FCDNet achieves impressive F1 scores of 78.51%, 90.48%, and 92.57% on the Forest Change Detection (FCD), LEVIR-CD, and WHU-CD datasets, respectively, delivering state-of-the-art results. This approach holds significant potential for advancing forest monitoring, providing a reliable tool for researchers and policymakers to better understand and protect forest ecosystems.

Abstract Pain is a complex phenomenon, the experience of which varies widely across individuals. At worst, chronic pain can lead to anxiety and depression. Cost-effective strategies are urgently needed to improve the treatment of pain, and thus we propose a novel home-based pain measurement system for the longitudinal monitoring of pain experience and variation in different patients with chronic low back pain. The autonomous nervous system and audio-visual features are analyzed from heart rate signals, voice characteristics and facial expressions using a unique measurement protocol. Self-reporting is utilized for the follow-up of changes in pain intensity, induced by well-designed physical maneuvers, and for studying the consecutive trends in pain. We describe the study protocol, including hospital measurements and questionnaires and the implementation of the home measurement devices. We also present different methods for analyzing the multimodal data: electroencephalography, audio, video and heart rate. Our intention is to provide new insights using technical methodologies that will be beneficial in the future not only for patients with low back pain but also patients suffering from any chronic pain.

Automatic pain recognition is paramount for medical diagnosis and treatment. The existing works fall into three categories: assessing facial appearance changes, exploiting physiological cues, or fusing them in a multi-modal manner. However, (1) appearance changes are easily affected by subjective factors which impedes objective pain recognition. Besides, the appearance-based approaches ignore long-range spatial-temporal dependencies that are important for modeling expressions over time; (2) the physiological cues are obtained by attaching sensors on human body, which is inconvenient and uncomfortable. In this paper, we present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. The framework is able to capture both local and long-range dependencies via the proposed attention mechanism for the learned appearance representations, which are further enriched by temporally attended physiological cues (remote photoplethysmography, rPPG) that are recovered from videos in the auxiliary task. This framework is dubbed rPPG-enriched Spatio-Temporal Attention Network (rSTAN) and allows us to establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases. It demonstrates that rPPG predictions can be used as an auxiliary task to facilitate non-contact automatic pain recognition.

Pain is a transient physical reaction that exhibits on human faces. Automatic pain intensity estimation is of great importance in clinical and health-care applications. Pain expression is identified by a set of deformations of facial features. Hence, features are essential for pain estimation. In this paper, we propose a novel method that encodes low-level descriptors and powerful high-level deep features by a weighting process, to form an efficient representation of facial images. To obtain a powerful and compact low-level representation, we explore the way of using second-order pooling over the local descriptors. Instead of direct concatenation, we develop an efficient fusion approach that unites the low-level local descriptors and the high-level deep features. To the best of our knowledge, this is the first approach that incorporates the low-level local statistics together with the high-level deep features in pain intensity estimation. Experiments are evaluated on the benchmark databases of pain. The results demonstrate that the proposed low-to-high-level representation outperforms other methods and achieves promising results.