  Detection of burn marks due to wildfires in inaccessible rain forests is
important for various disaster management and ecological studies. The
fragmented nature of arable landscapes and diverse cropping patterns often
thwart the precise mapping of burn scars. Recent advances in remote-sensing and
availability of multimodal data offer a viable solution to this mapping
problem. However, the task to segment burn marks is difficult because of its
indistinguishably with similar looking land patterns, severe fragmented nature
of burn marks and partially labelled noisy datasets. In this work we present
AmazonNET -- a convolutional based network that allows extracting of burn
patters from multimodal remote sensing images. The network consists of UNet: a
well-known encoder decoder type of architecture with skip connections commonly
used in biomedical segmentation. The proposed framework utilises stacked
RGB-NIR channels to segment burn scars from the pastures by training on a new
weakly labelled noisy dataset from Amazonia. Our model illustrates superior
performance by correctly identifying partially labelled burn scars and
rejecting incorrectly labelled samples, demonstrating our approach as one of
the first to effectively utilise deep learning based segmentation models in
multimodal burn scar identification.
