An ensemble of Convolutional Neural Networks for Land Use Land Cover Classification in the Amhara Region of Northwest Ethiopia using Very High-Resolution Commercial Satellite Imageries
Abstract
Mapping areas with complex topography and highly fragmented small-holder land use land cover (LULC) areas, such as the Amhara Region of Ethiopia, requires very high-resolution satellite imageries and robust classification methodologies. To this end, we have analyzed the pan-sharpened 2-meter very-high resolution (VHR) WorldView multi-temporal imageries using an ensemble of Convolutional Neural Networks (CNNs). Training and test LULC class datasets were obtained from the Land Investment for Transformation (LIFT) Ethiopia project that were digitized from centimeter-level special mission aerial photos. Several CNNs were used for the task of semantic segmentation and compared their performance to other machine learning algorithms such as decision tree models. Extensive geometric data augmentation and pre-processing techniques were performed to account for the spectral distribution of landscape features in the imagery at different dates. We have classified the region into eight broad land cover classes that comprise cropland, grassland, shrubland, forest, bareland, settlement, water, and wetland. While both LULC classification methods achieve sufficient overall accuracies, CNNs outperform the Random Forest algorithm in classifying the LULC of the study area. With CNNs, we were able to classify small features and classes with small occurrence in the training dataset by taking into consideration spatial features and class weighting. These research outputs will be beneficial in making informed decisions by the Amhara Region government sectors, and non-governmental Index-Insurance organizations on food security, market management, and in the crop insurance sector. We will use the cropland maps as a baseline for yield estimation and forecasting in our subsequent research work, which in turn can be used for recurrent seasonal yield estimation and forecasting that are useful for planning purposes to alleviate food insecurity.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMIN45B0373A