AGRICULTURAL LAND MAPPING AND CLASSIFICATION FROM SATELLITE IMAGES
Keywords:
Agricultural Land Mapping, Agricultural Land Classification, Agricultural Monitoring, Remote Sensing, Deep Learning, CNN, Satellite Imaging, ResNet, VGG-16, VGG-19.Abstract
Agricultural Land Mapping and Classification are among the most challenging
tasks in the agricultural domain. Accurate prediction of agricultural land type in developing
countries ahead of sowing is central to preventing famine, improving food security, and
sustainable development of agriculture. Currently, leading agricultural land use prediction
techniques mostly rely on locally sensed data, such as rainfall measurements and farmer
surveys from field visits. Locally sensed data provide detailed information but are expensive
to collect, often noisy, and extremely difficult to scale. Remote sensing and satellite imagery
data, a cheap and globallyaccessible resource, coupled with modern machine learning
approaches offer a potential solution. In this paper, we present a framework to work with
remote sensing and satellite imagery data to categorize land regions in terms of their
agricultural capabilities in order to maximize efficiency and productivity. Improving existing
methods, we incorporate a deep ensemble learning approach and combine multiple deep
learning methodologies to work with the potentially huge search spaces and navigate them
looking for optimal parametric combinations, extracting the best out of the underlying CNN
model. The model is actuated on satellite images acquired from the IKONOS Dataset
available in the public domain for research purposes.
