Deploying Large Language Models on edge platforms with mobile-oriented resource constraints faces challenges of limited resources and hallucination issues. While Retrieval-Augmented Generation (RAG) mitigates hallucinations through external knowledge, existing RAG systems on such platforms suffer from poor retrieval quality and lack standardized protocols. We propose a RAG framework for edge platforms with mobile-oriented resource constraints based on the Model Context Protocol (MCP), enabling plug-and-play access to heterogeneous knowledge bases. Our framework introduces a weighted voting fusion ranking mechanism integrating BERT-Recall, F1, Relaxed Exact Match (REM), and Query Relevance scores to enhance retrieval accuracy, combined with model quantization and few-shot learning for efficient on-device operation. Experiments on SQuAD, HotpotQA, and TriviaQA demonstrate that our framework achieves higher accuracy and lower error rates than state-of-the-art methods while maintaining low latency.

The accelerated non-agriculturalization of cropland has increasingly highlighted the importance of remote sensing (RS) change detection (CD) for monitoring land-use transitions. However, variations in RS imaging conditions and irregular cropland changes often result in noisy or inaccurate change maps. To address these challenges, we propose a novel deep learning framework named change-aware and fourier feature exchange Network (CAFENet). The method introduces a dedicated change-aware (CA) branch to extract discriminative change cues from pseudo-video sequences and integrates them into the backbone network. A fourier feature exchange module (FFEM) is designed to reduce brightness, color, and style discrepancies between bitemporal images, thereby enhancing robustness under varying acquisition conditions. Fused features are further refined using an efficient multi-scale attention mechanism (EMSA) to capture rich spatial details. In the decoding stage, a dynamic content-aware upsampling module (DCAU), together with skip connections, progressively recovers spatial resolution while preserving structural information. Experimental results on three datasets—CLCD, SW-CLCD, and LuojiaSET-CLCD—demonstrate that CAFENet achieves superior performance over state-of-the-art methods in terms of both accuracy and robustness, particularly in complex agricultural landscapes.

High carbon emissions (CE) have unbalanced the carbon cycle patterns on Earth, resulting in global warming and extreme weather events. However, the disparity and inequalities of carbon emissions have barely been explored from environmental justice perspective, which can help to formulate a more equitable carbon policy. Therefore, the satellite-based ODIAC dataset was adopted to first analyze the spatiotemporal characteristics of CE in China between 2010 and 2019; then the spatial disparities and environmental inequalities of CE were explored during the study period. The results show that CE in China increased by 13.74% during 2010-2019, with decadal mean of 91.49 million tons. The hotspots were observed in the eastern coastal and the northeastern regions. The spatial disparity decreased over the decade, with Theil index declining from 0.186 in 2010 to 0.163 in 2019. The inequality result indicates CE disproportionately affected populations living in low GDP per capita regions, while it became more equally distributed over time. The results suggest that the spatial and environmental inequality of CE need to be considered in policy formulation for a more equitable environment.

Nighttime Lights (NTLs) remote sensing imagery contains tremendous information and has been shown to accurately predict a region’s human dynamics, economic health and energy consumption. Despite its usefulness, NTLs imagery is less widely available than other remote sensing data modalities. Several challenges appear when attempting to reconstruct NTLs data, either from other data modalities or existing NTLs data. These include complex non-linear relationships between NTLs and multispectral bands, non-matching spatial and temporal coverage, and different atmospheric and cloud conditions. This study attempts to create an out-of-the-box model that compensates for missing NTLs data using widely available daytime data in a broadly generalizable manner. The proposed project has two objectives: the construction of an image-to-image dataset mapping daytime multispectral images (MODIS V6 Land Surface Reflectance, MODIS V6 Land Cover, MODIS V6 Vegetation Indices) to NTLs images, and the reconstruction of NTLs data using deep learning techniques by researching, creating, and employing the state-of-the-art architecture of the Mask Partial Convolutional Neural Network in conjunction with dilated convolutions. The project will facilitate the training of new models for predicting missing NTLs and make NTLs data more accessible for future remote sensing research.