Better understanding of Multi Domain Battle (MDB) challenges in complex military environments may start by gaining a basic scientific appreciation of the level of generalization and scalability offered by Machine Learning (ML) solutions designed, trained and optimized to achieve a single, specific task, continuously daytime and nighttime. We examine the generalization and scalability promises of a modern deep ML solution, applied to a unique spatial-spectral dataset that consists of blackbody calibrated, longwave infrared spectra of a fixed target site containing three painted metal surrogate tanks deployed in a field of mixed vegetation. Data was collected at roughly six minute intervals, nearly continuously, for over a year. This includes collection in many atmospheric conditions (rain, snow, sleet, fog, etc.) throughout the year. This paper focuses on data collected by a Telops Hyper-Cam from a 65 meter observation tower located at slant range of roughly 550 meters, from the targets. The dataset is very complex. There are no obvious spectral signatures from the target surfaces. The complexity is due in part to the natural variations of the various types of vegetation, cloud presence, and the changing solar loading conditions over time. This is precisely the environment MDB applications must function in. We detail some of the many training sets extracted to train different deep learning stacked auto encoder networks. We present performance results with receiver operator characteristic curves, confusion matrices, metric-vs-time plots, and classification maps. We show performance of ML models trained with data from various time windows, including over complete diurnal cycles and their performance processing data from different days and environmental conditions.