Geographic information systems (GIS) provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems (UAS), can exploit this and additional information such as building roof structure to improve navigation accuracy and safely perform contingency landings particularly in urban regions. However, building roof structure is not fully provided in maps. This paper proposes a method to automatically label building roof shape from publicly available GIS data. Satellite imagery and airborne LiDAR data are processed and manually labeled to create a diverse annotated roof image dataset for small to large urban cities. Multiple convolutional neural network (CNN) architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite image and LiDAR data fusion is shown to provide greater classification accuracy than using either data type alone. Model confidence thresholds are adjusted leading to significant increases in models precision. Networks trained from roof data in Witten, Germany and Manhattan (New York City) are evaluated on independent data from these cities and Ann Arbor, Michigan.

This paper presents a strategy for optimal estimation of parameters for towed cable systems using moving horizon estimation (MHE). The main contributions of the work include a novel formulation of MHE using a dead-band that explicitly rejects measurement noise, real-time implementation results, and the investigation of time varying stochastic disturbances as well as unknown yet constant disturbances. Further analysis is conducted on the observability and sensitivity of key parameters to determine which parameters can be estimated by the proposed approach using realtime streaming data from experiments. In addition to the real-time results, an offline multi-objective optimization is conducted to reveal the tradeoff between the computational burden and estimation variation. A Pareto frontier is used to demonstrate a series of optimal experimental configurations obtained by adjusting the model complexity (number of cable links and horizon steps). Finally, a multivariate parameter estimation is performed to explore the applicability of the proposed approach in simultaneously estimating multiple parameters. Index TermsUnmanned Aerial Vehicle (UAV), Parameter estimation, towed cable system, moving horizon estimation, aerial recovery, multi-objective optimization.

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