In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field.

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths.

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers.

Radar odometry has been gaining attention in the last decade. It stands as one of the best solutions for robotic state estimation in unfavorable conditions; conditions where other interoceptive and exteroceptive sensors may fall short. Radars are widely adopted, resilient to weather and illumination, and provide Doppler information which make them very attractive for such tasks. This article presents an extensive survey of the latest work on ground-based radar odometry for autonomous robots. It covers technologies, datasets, metrics, and approaches that have been developed in the last decade in addition to in-depth analysis and categorization of the various methods and techniques applied to tackle this problem. This article concludes with challenges and future recommendations to advance the field of radar odometry making it a great starting point for newcomers and a valuable reference for experienced researchers.

Current waste sorting mechanisms, particularly those relying on manual processes, semi-automated systems, or technologies without Artificial Intelligence (AI) integration, are hindered by inefficiencies, inaccuracies, and limited scalability, reducing their effectiveness in meeting growing waste management demands. This study introduces a prototype waste sorting machine that integrates an AI-driven vision system with a Programmable Logic Controller (PLC) for high-accuracy automated waste sorting. The system, powered by the YOLOv8 deep learning model, achieved sorting accuracies of 88% for metal cans, 75% for paper, and 91% for plastic bottles, with an overall precision of 90%, a recall of 80%, and a mean average precision (mAP50) of 86%. The vision system provides real-time classification, while the PLC manages conveyor and actuator operations to ensure seamless sorting. Experimental results in a controlled environment validate the system’s high accuracy, minimal processing delays, and scalability for industrial recycling applications. This innovative integration of AI vision with PLC automation enhances sorting efficiency, reduces ecological impacts, and minimizes labor dependency. Furthermore, the system aligns with sustainable waste management practices, promoting circular economy principles and advancing the Sustainable Development Goals (SDGs).

This study investigates the best available methods for remote monitoring inland small-scale waterbodies, using remote sensing data from both Landsat-8 and Sentinel-2 satellites, utilizing a handheld hyperspectral device for ground truthing. Monitoring was conducted to evaluate water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and turbidity. Ground truthing was performed to select the most suitable atmospheric correction technique (ACT). Several ACT have been tested: dark spectrum fitting (DSF), dark object subtraction (DOS), atmospheric and topographic correction (ATCOR), and exponential extrapolation (EXP). Classical sampling was conducted first; then, the resulting concentrations were compared to those obtained using remote sensing analysis by the above-mentioned ACT. This research revealed that DOS and DSF achieved the best performance (an advantage ranging between 29% and 47%). Further, we demonstrated the appropriateness of the use of Sentinel-2 red and vegetation red edge reciprocal bands for estimating Chl-a ( , ). As for Landsat-8, red to near-infrared ratio ( ) produced the best performing model ( , ), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model ( , NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with