Exploration of Convolutional Neural Network Architectures for Large Region Map Automation

Deep learning semantic segmentation algorithms have provided improved frameworks for the automated production of Land-Use and Land-Cover (LULC) maps, which significantly increases the frequency of map generation as well as consistency of production quality. In this research, a total of 28 different model variations were examined to improve the accuracy of LULC maps. The experiments were carried out using Landsat 5/7 or Landsat 8 satellite images with the North American Land Change Monitoring System labels. The performance of various CNNs and extension combinations were assessed, where VGGNet with an output stride of 4, and modified U-Net architecture provided the best results. Additional expanded analysis of the generated LULC maps was also provided. Using a deep neural network, this work achieved 92.4% accuracy for 13 LULC classes within southern Manitoba representing a 15.8% improvement over published results for the NALCMS. Based on the large regions of interest, higher radiometric resolution of Landsat 8 data resulted in better overall accuracies (88.04%) compare to Landsat 5/7 (80.66%) for 16 LULC classes. This represents an 11.44% and 4.06% increase in overall accuracy compared to previously published NALCMS results, including larger land area and higher number of LULC classes incorporated into the models compared to other published LULC map automation methods

Memory Influences Visual Cognition across Multiple Functional States of Interactive Cortical Dynamics

No embargo requiredMemory supports a wide range of abilities from categorical perception to goal-directed behavior, such as decision-making and episodic recognition. Memory activates fast and surprisingly accurately and even when information is ambiguous or impoverished (i.e., showing object constancy). This paper proposes the multiple-state interactive (MUSI) account of object cognition that attempts to explain how sensory stimulation activates memory across multiple functional states of neural dynamics, including automatic and strategic mental simulation mechanisms that can ground cognition in modal information processing. A key novel postulate of this account is ‘multiple-function regional activity’: The same neuronal population can contribute to multiple brain states, depending upon the dominant set of inputs at that time. In state 1, the initial fast bottom-up pass through posterior neocortex happens between 95 ms and ~200 ms, with knowledge supporting categorical perception by 120 ms. In state 2, starting around 200 ms, a sustained state of iterative activation of object-sensitive cortex involves bottom-up, recurrent, and feedback interactions with frontoparietal cortex. This supports higher cognitive functions associated with decision-making even under ambiguous or impoverished conditions, phenomenological consciousness, and automatic mental simulation. In the latest state so far identified, state M, starting around 300 to 500 ms, large-scale cortical network interactions, including between multiple networks (e.g., control, salience, and especially default mode), further modulate posterior cortex. This supports elaborated cognition based on earlier processing, including episodic memory, strategic mental simulation, decision evaluation, creativity, and access consciousness. Convergent evidence is reviewed from cognitive neuroscience of object cognition, decision-making, memory, and mental imagery that support this account and define the brain regions and time course of these brain dynamics