Current road extraction models from remote sensing images based on deep learning are computationally demanding and memory-intensive because of their high model complexity, making them impractical for mobile devices. This study aimed to develop a lightweight and accurate road extraction model, called Road-MobileSeg, to address the problem of automatically extracting roads from remote sensing images on mobile devices. The Road-MobileFormer was designed as the backbone structure of Road-MobileSeg. In the Road-MobileFormer, the Coordinate Attention Module was incorporated to encode both channel relationships and long-range dependencies with precise position information for the purpose of enhancing the accuracy of road extraction. Additionally, the Micro Token Pyramid Module was introduced to decrease the number of parameters and computations required by the model, rendering it more lightweight. Moreover, three model structures, namely Road-MobileSeg-Tiny, Road-MobileSeg-Small, and Road-MobileSeg-Base, which share a common foundational structure but differ in the quantity of parameters and computations, were developed. These models varied in complexity and were available for use on mobile devices with different memory capacities and computing power. The experimental results demonstrate that the proposed models outperform the compared typical models in terms of accuracy, lightweight structure, and latency and achieve high accuracy and low latency on mobile devices. This indicates that the models that integrate with the Coordinate Attention Module and the Micro Token Pyramid Module surpass the limitations of current research and are suitable for road extraction from remote sensing images on mobile devices.

The surface reflectance is of important geophysical parameter for many quantitative remote sensing applications. The inference of earth surface spectral reflectance using visible observations is complicated mainly because of scattering and absorption effects due to gases and aerosols. Surface reflectance product from MODIS data has been operationally offered, however, aerosol effect removal is based DDV algorithm which is restrictedly used for lower reflectance ground surface such as dense vegetation. In this paper we attempt a solution to the problem of retrieval of surface reflectance by exploiting the synergy of TERRA and AQUA MODIS data. Preliminary results of retrieval experiments and validation is encouraging and showed promising potential to address surface reflectance retrieval for land even for higher reflective surface.