Using satellite monitoring data, researchers have developed a deep learning algorithm that could provide real-time monthly land use and land cover maps for parts of India. Between the 1890s and the 1990s, a combination of rapid economic development and overexploitation of local resources led to India losing nearly 80% of its native forest area. The land use monitoring system was trained using data provided by Norway's International Climate and Forests Initiative, an enterprise of the Norwegian government that aims to reduce the destruction of tropical forests, in part by providing high-resolution images of the world's tropics. By combining the NICFI products' data with a global land cover map produced by Tsinghua University, their deep learning model was able to acquire a more detailed type of base map of the area. "The process helps us to assimilate the two datasets so they can be used to train our deep learning model." This essentially merges thousands of small pictures into one larger base map. After training their deep learning model on these new satellite images, the team was able to process 10 base maps of the area, ranging from January to October 2022. During her presentation, Zuo said that using these maps, the team was able to detect seasonal shifts across India, such as changes to barren land, how crop land was affected by monsoons during the rainy season, and the distribution of forests in mountainous regions. One conclusion from the study was that it is vital for ecologists to more closely study the seasonal impact of monsoons on India's forest cover. Understanding these seasonal changes can help scientists understand the effects of climate change on forests