Deep learning is a powerful technique for the analysis of remote sensing imagery. For applications that require real-time processing on mobile platforms, a low power consumption processing unit is advantageous. The human brain is remarkably powerful at image recognition tasks while operating at very low power consumption levels. Neuromorphic computing designs aim to achieve energy efficiency through the use of spiking neurons and low-precision synapses to perform data processing. We demonstrate here the classification of red, green, blue and depth and hyperspectral data sets using a neuromorphic processing unit (IBM TrueNorth Neurosynaptic System). The convolutional neural-network architecture of the classifier network has been adapted to fit the neuromorphic architecture. The results on overhead imagery and hyperspectral imagery data show that neuromorphic platforms can achieve the state-of-the-art performance in semantic labeling with significantly ( 1000 ×) lower power consumption than traditional GPU-based solutions.