# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion  
[PERSON]  
1 Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (R.K.M. ); [EMAIL_ADDRESS] (H.M.)  
[PERSON]  
2 Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (R.K.M. ); [EMAIL_ADDRESS] (H.M.)  
[PERSON]  
1 Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (R.K.M. ); [EMAIL_ADDRESS] (H.M.)  
[PERSON]  
1 Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (R.K.M. ); [EMAIL_ADDRESS] (H.M.)  
[PERSON]  
2 Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (A.P. ); rkmente@nscu. A broad range of cross-disciplinary applications (e.g., classification, change detection) utilize the synthetic fusion of multi-source data that range in spatial, temporal, and spectral resolutions [6] to improve the availability and accuracy of geospatial data. In remote sensing, multi-source data fusion methods have been characterized into three distinct levels: pixel, feature, and decision [6; 7]. Pixel level fusion combines raw data from multiple sources into a new dataset of the same resolution. Feature level fusion works by combining extracted features (e.g., texture, edges) from various datasets into a new dataset and helps reduce data with high dimensionality. Decision level fusion is performed by fusing multiple modeled results (e.g., confidence intervals, categories) into a new decision dataset. High-level fusion methods (e.g. feature, decision) combine multiple-source data such as multispectral and hyperspectral data, GIS data, synthetic aperture radar (SAR), and lidar data to improve the accuracy of land cover image classification of high-resolution data [6; 8]. However, the synthetic utilization of multi-source data comes with many technical challenges that are only increasing as the abundance of potential data sources and new sensor systems grow. Multi-source multi-temporal land cover classification and change detection face challenges in co-registration as traditional earth observation platforms (e.g., Landsat, Sentinel-2) are fused or replaced with new satellite constellations. Satellite constellations such as PlanetScope [9] are made up of many satellites that individually capture small scenes that generally need to be patched into a mosaic for analysis. However, special consideration is needed to ensure the geometric and radiometric accuracy of the data because of potential differences in platforms and sensors and other environmental factors [10]. Current research in deep learning is working to improve multi-source and multi-temporal image registration by identifying features with a high degree of self-similarity at multiple spatial scales after traditional geographic registration is performed [8]. Other research in multi-source multi-temporal land cover classification is examining high-level fusion methods to utilize crowd-scoured GIS vector data such as OpenStreetMap (OSM) to enhance and augment land cover classification of remotely sensed imagery. These methods include augmenting classification results with high accuracy GIS data during post-processing, the generation of model training samples, and feature level fusion of GIS data to improve image segmentation [11; 12; 13]. Research is also being done on the reverse effect where optical imagery is being used with modified cyclically-trained generative adversarial networks (CycleGAN) [14] to update OSM [15]. In addition to optical imagery, the synthetic fusion of multiple-source digital elevation model (DEM) data requires its own set of considerations.