Abstract: Deep learning has great potential for environmental monitoring, yet real-world applications often face challenges from large-scale, multimodal, and noisy datasets. We introduce GreenNet, a flexible and open-source framework that makes it easier to build and scale deep learning models for remote sensing and environmental data. GreenNet offers reusable neural network modules, simple data integration tools, and built-in explainability features tailored for geospatial applications. To demonstrate its effectiveness, we apply it to case studies such as deforestation detection, urban heat island mapping, and air quality forecasting. These examples show that GreenNet delivers strong predictive performance while significantly reducing the effort needed to develop models. By connecting domain-specific data processing with modern deep learning techniques, GreenNet aims to make AI more accessible, reproducible, and interpretable for researchers and practitioners in environmental science.
The integration of Unmanned Aerial Vehicles equipped with hyperspectral and multispectral sensors has revolutionized remote sensing in agriculture, forestry, and disaster management. However, hyperspectral imaging generates extraordinarily dense, high-dimensional datasets that ov…
Proposes a zero-copy architecture that eliminates the CPU/GPU data transfer bottleneck in Physics-AI workloads by leveraging Apple Silicons unified memory. Describes a pipeline where particle simulation data (OpenFPM/Metal) resides in shared memory that MLX neural networks can re…
The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep lear…
This position paper formally establishes the principles of Structural Copyright and Explainability within Technorhetoric Version 3.0. Generative AI systems increasingly replicate not only textual content but also underlying conceptual and rhetorical structures. These structures c…