  Acquiring information on large areas on the earth's surface through satellite
cameras allows us to see much more than we can see while standing on the
ground. This assists us in detecting and monitoring the physical
characteristics of an area like land-use patterns, atmospheric conditions,
forest cover, and many unlisted aspects. The obtained images not only keep
track of continuous natural phenomena but are also crucial in tackling the
global challenge of severe deforestation. Among which Amazon basin accounts for
the largest share every year. Proper data analysis would help limit detrimental
effects on the ecosystem and biodiversity with a sustainable healthy
atmosphere. This report aims to label the satellite image chips of the Amazon
rainforest with atmospheric and various classes of land cover or land use
through different machine learning and superior deep learning models.
Evaluation is done based on the F2 metric, while for loss function, we have
both sigmoid cross-entropy as well as softmax cross-entropy. Images are fed
indirectly to the machine learning classifiers after only features are
extracted using pre-trained ImageNet architectures. Whereas for deep learning
models, ensembles of fine-tuned ImageNet pre-trained models are used via
transfer learning. Our best score was achieved so far with the F2 metric is
0.927.
