The Astrobees are free-flying robots that operate inside the International Space Station (ISS) and were launched to the ISS in 2019. Since then they have successfully performed hundreds of activities in space supporting almost two dozen separate research projects. The robots were designed to overcome multiple challenges unique to the ISS environment, including safety, upgradeability and maintainability, limited mass and computation, and unique localization challenges from lack of gravity and a constantly changing environment. This article provides an overview of Astrobee, from hardware and software design to deployment results and activities.

The NASA Ames Stereo Pipeline (ASP) is an open source software of automated geodesy and photogrammetry tools to process satellite, aerial camera, and historical imagery with and without accurate camera pose information using a structure-from-motion (SfM) methodology. ASP is designed to generate topographic digital surface models (DSM). We added to the ASP topographic module a bathymetric module to derive near shore bathymetry using satellite panchromatic bands (PAN), and multispectral bands. The process is semiautomatic and can generate either topographic, bathymetric or topo-bathymetric (TB) seamless 3D point clouds and DSM in the same vertical and horizontal coordinate systems. The bathymetric results depend heavily on the water surface elevation and while previous methods considered the water surface horizontal, our bathymetric module takes into consideration the earth curvature for the considered satellite imagery. A land / water mask can be automatically derived using NIR bands or can be user defined. The new ASP bathymetry module was tested using WorldView-2 panchromatic and green band stereo imagery in Florida Keys (Key West) FL from May 2015. The nearshore PAN and GRN bathymetric results around Key West, FL were validated against bathymetric lidar collected in 2017. The validation errors improve with adding camera calibration and finally alignment to prior topographic lidar data (no bathymetry data used) from 1.0778 m root mean square error (RMSE) to 0.4052 m RMSE to 0.2480 m RMSE, respectively. For PAN bands the depth penetration around Key West was around 4 m with a TB DSM resolution of 1 m. For the same area, using the green (GRN) band the bathymetric validation RMSE in absence of camera adjustment or alignment was 1.1040 m, with camera adjustment RMSE improves to 0.6846 m, and with topographic alignment RMSE is 0.5854. For the green bands the depth penetration in Key West was approximately 7 m with a TB DSM resolution of 2 m.

Image feature tracking with medium-resolution optical satellite imagery (e.g., Landsat-8) offers measurements of glacier surface velocity on a global scale. However, for slow-moving glaciers (<0.1 m/day), the larger pixel sizes (~15-30 m) and longer repeat intervals (minimum of 16 days, assuming no cloud cover) limit temporal sampling, often precluding analysis of sub-annual velocity variability. As a result, detailed records of short-term glacier velocity variations are limited to a subset of glaciers, often from dedicated SAR image tasking and/or field observations. To address these issues, we are leveraging large archives of very-high-resolution (~0.3-0.5 m) DigitalGlobe WorldView/GeoEye imagery with ~monthly repeat interval and high-resolution (~3-5 m) Planet PlanetScope imagery with ~daily-weekly repeat interval for the period from 2014 to 2019. We are using automated, open-source tools to develop corrections for sensor geometry and image geolocation, and integrating new, high resolution DEMs for improved orthorectification, reducing the uncertainty of short-term (monthly to seasonal) velocity measurements. These temporally dense records will be integrated with other velocity products (e.g., NASA ITS_LIVE), which will allow us to study the evolution of glacier dynamics, and its relationships with local climatology, geomorphology, and hydrology on a regional scale. In this study, we present initial results for surface velocity mapping for glaciers in Khumbu Himalaya, Nepal and Mt. Rainier, USA. We are using high-performance computing environments to scale this analysis to larger glacierized regions in High Mountain Asia and Continental U.S.

We are developing a fully automated Structure from Motion (SfM) processing pipeline to generate high-resolution digital elevation models (DEMs) from archives of historical aerial and satellite imagery acquired between the 1950s to the 1990s. Scanned images are loaded directly from online archives and processed using open-source software deployed on cloud-computing infrastructure. Modern DEMs with high resolution and accuracy (e.g., airborne lidar, stereo DEMs from sub-meter satellite imagery) are used to iteratively improve historical image geolocation, without manual processing steps involving ground control. We present preliminary DEM and orthoimage time series for glaciers in Washington state, derived from the USGS North American Glacier Aerial Photography (NAGAP) archive. These records document surface elevation change with sub-meter vertical accuracy over decadal timescales. We are using these observations to quantify evolving rates of glacier mass change and proglacial sediment transport. Our aim is to generate long-term, regional records of glacial response to climate forcing on decadal timescales. Better understanding these past responses will help constrain projections of future glacier change under different climate scenarios, as well as impacts on downstream water resources.