Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

Soil and Crop/Tree Segmentation from Remotely Sensed Data by using Digital Surface Models

The increased availability of high resolution remote sensor data for precision agriculture 1 applications permits users to aquire deeper and more relevant knowledge about crops states that lead 2 inevitably to better decisions. The algorithm libraries being developed and evolved around these 3 applications rely on multi-spectral or hyper-spectral data acquired by using manned or unmanned 4 plat...

Remotely Sensed Evapotranspiration Data Assimilation for Crop Growth Modeling

The agricultural sector will require more water in the near future to provide more food, fibre and fuels (Molden et al., 2007). As population increases and development calls for increased demand of food, a change in diet due to increased prosperity, and a recent focus on biofuels. This population growth coupled with industrialization and urbanization will result in an increasing demand for wate...

Analysis of Remotely Sensed Data for Detecting Soil Limitations

During 1971 and 1972 a detailed study was conducted on a fallow field in the proposed Oahe Irrigation Project to determine the relationship between the tonal variation observed on aerial photographs and the properties of eroded soil. Correlation and regression analysis of digitized, multiemulsion, color infrared film (2443) data and detailed field data revealed a highly significant correlation ...

Spatiotemporal Estimation of PM2.5 Concentration Using Remotely Sensed Data, Machine Learning, and Optimization Algorithms

PM 2.5 (particles <2.5 μm in aerodynamic diameter) can be measured by ground station data in urban areas, but the number of these stations and their geographical coverage is limited. Therefore, these data are not adequate for calculating concentrations of Pm2.5 over a large urban area. This study aims to use Aerosol Optical Depth (AOD) satellite images and meteorological data from 2014 to 2017 ...

Remotely Sensed Data Segmentation under a Spatial Statistics Framework