# A high-resolution global basemap of (101955) Bennu

C. A. Bennett, D. N. Dellagiustina, K. J. Becker, T. L. Becker, K. L. Edmundson, D. R. Golish, R. J. Bennett, K. N. Burke, C. N. U. Cue, B. E. Clark, et al.

► To cite this version:

C. A. Bennett, D. N. Dellagiustina, K. J. Becker, T. L. Becker, K. L. Edmundson, et al., A high-resolution global basemap of (101955) Bennu. Icarus, 2021, 357, 10.1016/j.icarus.2020.113690. insu-03713794

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# 1 A High-Resolution Global Basemap of (101955) Bennu

C. A. Bennett¹, D. N. DellaGiustina¹, K. J. Becker¹, T. L. Becker¹, K.L. Edmundson¹, D. R Golish¹, R. J.

Bennett¹, K. N. Burke¹, C. N. U. Cue¹, B. E. Clark², J. Contreras¹, J. D. P. Deshapriya³, C. Drouet

d'Aubigny¹, G. Fitzgibbon¹, E. R. Jawin⁴, T. Q. Nolan¹, N. A. Porter¹, M. M. Riehl¹, H. L. Roper¹, B.

Rizk¹, Y. Tang¹, Z. Zeszut¹, R. W. Gaskell⁵, E. E. Palmer⁵, J. R. Weirich⁵, M. M. Al Asad⁶, L. Philpott⁶,

M. G. Daly⁷, O. S. Barnouin⁸, H. L. Enos¹, D. S. Lauretta¹

¹Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ, USA.

²Ithaca College, Ithaca, NY, USA.

³LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Univ. Paris Diderot, Sorbonne Paris Cité, Meudon, France.

⁴Smithsonian Institution National Museum of Natural History, Washington, DC, USA.

⁵Planetary Science Institute, Tucson, AZ, USA.

⁶Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC, Canada.

⁷The Centre for Research in Earth and Space Science, York University, Toronto, ON, Canada.

⁸The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.

¹ Corresponding Author:

Carina A. Bennett (carinaj@orex.lpl.arizona.edu)

1415 N. 6th Ave. Tucson, AZ 85705

# 22 Abstract

In early 2019, NASA's OSIRIS-REx (Origins, Spectral Interpretation, Resource

Identification, and Security-Regolith Explorer) mission surveyed asteroid (101955) Bennu with a collection of instruments, including the OSIRIS-REx Camera Suite (OCAMS) PolyCam imager.

Using PolyCam panchromatic images, we constructed a globally controlled basemap of Bennu at an approximate ground sample distance of 5 cm with a mean spatial accuracy of ~30 cm. The basemap was photometrically normalized using a Minneart phase angle correction. New mapping methods were developed to combine images of Bennu's irregular shape and extremely rough surface into a nearly seamless mosaic. Here we present the global basemap of Bennu and discuss the image processing techniques used to construct a high-resolution mosaic of an irregular small body.

# 34 Keywords

Asteroids; Asteroid Surfaces; Image Processing; Near-Earth Objects
## Introduction

NASA's OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification and Security-Regolith Explorer) spacecraft launched on September 8, 2016. It began the Approach phase of the mission on August 17, 2018, when it acquired the first images of asteroid (101955) Bennu as a point source from over 2 million km away. In early 2019, the Detailed Survey phase of the mission characterized Bennu's surface [Lauretta et al., 2017]. This phase included a global mapping campaign intended to gather data for a high-resolution basemap.

As OSIRIS-REx is a sample return mission, a basemap is needed to identify a safe and sampleable location for the spacecraft's sample acquisition maneuver [Berry et al., 2013; Williams et al., 2018; Lauretta et al., 2017]. Therefore, the global basemap must have sufficient resolution to clearly show geologic features including craters, ridges, boulders, and any other potentially hazardous objects. Here we describe the acquisition, selection, and processing of the OSIRIS-REx Camera Suite (OCAMS) [Rizk et al., 2018] images that contributed to the basemap.
We also outline the steps taken to perform global registration of the images to a 3D tessellated shape model of the asteroid and to one another. We developed a series of techniques to combine data from different campaigns that imaged Bennu's irregular and rough surface into a single, nearly seamless, and photometrically controlled mosaic. The end result is a basemap, from 90° to -90° latitude and 0° to 360° longitude, at 5 cm per pixel. This is the finest spatial scale at which a planetary body has been globally mapped.

## Asteroid (101955) Bennu

Bennu was discovered on September 11, 1999. It is a near-Earth asteroid with an apoapsis of 1.356 AU and a periapsis of 0.897 AU [Lauretta et al. 2015]. It exhibits a spinning-top-like shape with an equatorial extent of 564.73±0.12 x 536.10±0.12 m and a polar extent of 498.49±0.12 m [Lauretta & DellaGiustina et al., 2019], making it the smallest object orbited by a spacecraft.

Bennu is heavily cratered [Walsh et al., 2019]. The surface is rough and densely populated with large (>1 m) boulders, including more than 200 boulders > 10 m [DellaGiustina & Emery et al., 2019]. Bennu's morphology, spinning-top shape with a prominent equatorial ridge,
and macroporosity, which ranges from 25% to 50% [Barnouin et al., 2019], are consistent with its characterization as a 'rubble pile'. The asteroid's surface reflectance indicates that it is one of the darkest objects in the Solar System, with an average geometric albedo of 4.4% [DellaGiustina and Emery et al., 2019; Hergenrother et al., 2019]. There is, however, a measurable range of albedo variation across the surface, with some boulders reaching normal albedos >25% [Lauretta and DellaGiustina et al., 2019; DellaGiustina et al., 2020; Material from (4) Vesta on Asteroid (101955) Bennu", in review, *Nature Astronomy*]. We also observe a heterogeneity amongst rock types, where some boulders exhibit fracturing patterns with angular features, whereas others have a distinctly hummocky, or cauliflower-like, texture [Molaro et al. 2019, "In situ evidence of thermally induced rock breakdown widespread on Bennu's surface", in review, *Nature Communications*].

In addition to the sample return requirement, the OSIRIS-REx mission goals included mapping of >80% of the asteroid's surface for scientific investigation [Lauretta et al. 2017; DellaGiustina et al., 2018]. These maps provide geologic context for thematic maps that integrate data from other OSIRIS-REx instruments and for the higher-resolution local maps of the potential sample sites.

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## Image Acquisition

The global panchromatic basemap is composed of radiometrically calibrated images [Golish et al., 2020] in units of reflectance (also known as I/F or radiance factor) acquired by the OCAMS PolyCam imager [Rizk et al., 2018]. Global PolyCam imaging campaigns targeted a specific sub-spacecraft latitude and subsequently acquired images while scanning along lines of constant longitude above and below that targeted latitude. This work follows IAU recommendations for cartographic coordinates where latitude and longitude angles are measured from the body center. "North", or +90° latitude, is defined such that it follows the righthand direction of rotation with longitude increasing eastwardly [Archinal et al., 2018; Barnouin et al. 2019a]. The campaigns were performed using a series of hyperbolic flybys described by DellaGiustina et al. (2018). Each campaign, designated by flyby number, took place on eight days throughout the Baseball Diamond leg of the Detailed Survey mission phase [Lauretta et al., 2017; DellaGiustina et al., 2018]. Most images that contribute to the global
95 panchromatic basemap were acquired during Flybys 3, 4A, and 4B. Select data from Flybys 5A
96 and 6A were also incorporated to provide better coverage in high-latitude regions that were
97 poorly illuminated in the earlier flybys.

98 The Flyby 3 (March 21, 2019) imaging station was targeted for an equatorial sub-
99 spacecraft latitude with an average altitude of 3.46 km. The spacecraft began observations at a
100 local solar time (LST) of 10 am on Bennu (40° phase angle). As the asteroid rotated, the
101 spacecraft slewed north and south, resulting in coverage between approximately 70 and -70°
102 latitude. Because of Bennu's equatorial ridge and spinning-top shape, Flyby 3 images > 20 and <
103 -30° latitude suffer from foreshortening and appear distorted when map-projected. As a result,
104 those data contribute only to the equatorial latitudes between 20 and -30° of the global
105 basemap.

106 Flybys 4A and 4B took place on March 28 and March 29, 2019, respectively. Both had an
107 average spacecraft altitude of 3.51 km. During Flyby 4A, the OSIRIS-REx spacecraft began image
108 acquisition at a sub-spacecraft latitude of -40° and slewed north and south, collecting images of
109 the southern hemisphere from -90 to 0° latitude. Flyby 4B provided analogous data in the
110 northern hemisphere with a sub-spacecraft latitude of 40°. Like Flyby 3, Flybys 4A and 4B
111 observed the surface of Bennu at 10 am LST (48° phase angle).

112 Flybys 5A and 6A (April 4 and April 11, 2019) were not designed to contribute to the
113 global basemap and instead were intended as inputs to the global shape model and digital
114 terrain maps [Barnouin et al., 2019b]. A subset of images from these campaigns, however, had
115 sufficiently comparable geometry to Flybys 3 and 4 that they could be incorporated into select
116 locations. Similar to Flybys 4A and 4B, Flyby 5A was a southern hemisphere campaign, positioned
117 at -40° latitude, whereas Flyby 6A was positioned to image the northern hemisphere with a sub-
118 spacecraft latitude of 40°. For both Flybys 5A and 6A, the images were taken with PolyCam
119 pointing at 12:30 LST, resulting in phase angles of ~39°.

120 Table 1 describes the relevant imaging conditions and statistics for basemap input images
121 per flyby. The imaging campaigns for Flybys 3 and 4A/B were designed to highlight surface
122 terrain and geologic features and serve as the main input to the hazard and feature identification
123 activities of the OSIRIS-REx mission. PolyCam images that contribute to the basemap have an
approximate pixel scale of 5 cm. The mean oblique pixel scale, which is calculated using the emission angle at that pixel and better represents the actual scale when the camera is pointed at oblique emission angles, is ~5.37 cm. Phase angle ranges from 30 to 50° in the equatorial to mid-latitudes; this results in shadows that define boulders, crater walls, and other terrain without obscuring significant portions of the surface. In some instances, we selected additional images at the lowest available incidence angle (12:30 pm LST). These images were sourced from Flybys 5A and 6A and were only incorporated in areas with significant shadowing—mostly near the poles and in the vicinity of exceptionally large boulders (>20 m).

<table><caption>Table 1: Imaging conditions and statistics for images included in the global basemap per flyby.</caption><thead><tr><th>Flyby No.</th><th>Date (MM-DD-YYYY)</th><th>No. of Total PolyCam Images</th><th>No. of Images Used in Basemap</th><th>Mean Spacecraft Altitude</th><th>Mean Phase Angle</th><th>Mean Emission Angle</th><th>Mean Incidence Angle</th><th>Mean Pixel Scale (m/pixel)</th><th>Mean Oblique Pixel Scale (m/pixel)</th><th>Mean Sub-spacecraft Latitude</th></tr></thead><tbody><tr><td>3</td><td>03-21-2019</td><td>1365</td><td>641</td><td>3.46km</td><td>30.46°</td><td>14.70°</td><td>34.03°</td><td>0.047</td><td>0.049</td><td>1.62°</td></tr><tr><td>4A</td><td>03-28-2019</td><td>976</td><td>754</td><td>3.52km</td><td>48.66°</td><td>23.18°</td><td>55.34°</td><td>0.050</td><td>0.057</td><td>-38.29°</td></tr><tr><td>4B</td><td>03-29-2019</td><td>1037</td><td>751</td><td>3.52km</td><td>48.46°</td><td>22.62°</td><td>52.07°</td><td>0.048</td><td>0.054</td><td>41.72°</td></tr><tr><td>5A</td><td>04-04-2019</td><td>837</td><td>3</td><td>4.73km</td><td>39.35°</td><td>29.93</td><td>64.54</td><td>0.064</td><td>0.077</td><td>41.92°</td></tr><tr><td>6A</td><td>04-11-2019</td><td>837</td><td>6</td><td>4.74km</td><td>39.60°</td><td>28.48</td><td>65.19</td><td>0.065</td><td>0.074</td><td>-37.49°</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>Figure 1: Diagram showing the workflow to create the global basemap.</figcaption></figure>
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## Image Calibration and Pre-Processing

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Raw OCAMS images were initially processed by an automated calibration pipeline (Figure 1, pink box) [Golish et al., 2019]. Bias and dark counts were subtracted, then bad pixels and charge smear were removed, followed by a flat field normalization. Images used in the basemap were converted into units of reflectance. After calibration, further image processing was performed using ISIS3 applications [Keszthelyi et al., 2013; https://isis.astrogeology.usgs.gov/]. Figure 1 shows the specific ISIS3 applications used for these pre-processing steps (identified by rounded rectangles). Many of these applications have been modified by the OSIRIS-REx mission for use with OCAMS data and 3D tessellated shape models [DellaGiustina et al., 2018] and are indicated with a red border in Figure 1.

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## Digital Terrain Model

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Bennu has an irregular shape that cannot be accurately represented by a sphere or an ellipsoid. Hence, we rectified OCAMS images using a 3D tessellated digital terrain model (DTM) [Barnouin et al., 2019] with an average facet size of approximately 80 cm. Using a version of ISIS3 that supports planetary shapes described by tessellated plate models [DellaGiustina et al., 2018], we calculated photometric angles as well as body-fixed planetocentric, planetographic, and cartesian coordinates per pixel at the facet scale of the DTM.

To create the basemap, we used a global DTM with 3,145,728 facets (v28), which represents an update to the shape model presented in Barnouin et al (2019a). The DTM was generated from 16161 images acquired between November 1, 2018, and April 4, 2019, including a subset of images that contributed to the global basemap, using techniques described by Barnouin et al. (2019a, 2019b). This DTM has an estimated lateral accuracy of 0.519 m and radial accuracy of 0.644 m. Additionally, higher-resolution local DTMs were used during the photogrammetric control process to tie images to the DTM ("ground"). Most local DTMs consisted of either 500,000 or 720,000 facets, and the prime meridian [Barnouin et al., 2019a] DTM had 156,800 facets. Local DTMs were registered to the global DTM.
166 Photogrammetric Control

167 OCAMS images were photogrammetrically controlled to correct for spacecraft position
168 and camera pointing uncertainties in reconstructed SPICE kernels. This process improved the
169 registration between the images and the DTM ("image-to-ground") and between the images
170 themselves ("image-to-image"). Image features were manually registered to the DTM to create
171 ground control points (GCPs). An example of a GCP can be seen in Figure 2. Additionally, images
172 were registered to one another by identifying features common within overlaps known as tie
173 points (TPs). TP image measurement was accomplished through automated feature detection.
174 The three-dimensional TP and GCP coordinates and their corresponding two-dimensional
175 sample/line image measurements (or image measures) form a control network which, along with
176 initial values for spacecraft position and camera pointing parameters, serve as input to the ISIS3
177 application jigsaw [Edmundson et al., 2012]. jigsaw performs a process called bundle
178 adjustment, which uses least-squares estimation to simultaneously solve for refined pointing and
179 position parameters and the triangulated coordinates of all TPs and GCPs [Brown, 1976]. Below
180 we describe the acquisition and measurement of TPs and GCPs, the resulting control network,
181 and bundle adjustment results. Figure 1 illustrates the flow of the image-to-ground and image-
182 to-image control processes.

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184 Image-to-Ground Control

185 For the selection and measurement of GCPs, we manually registered PolyCam images to
186 the local DTMs. Because the local DTMs are registered to the global DTM, images manually
187 registered to them are globally aligned. Because of the mission requirement to quickly evaluate
188 and down-select among the potential sample sites, local DTMs were only created for the prime
189 meridian and potential sample sites. As a result, GCP measurements were acquired for 32
190 locations, but are not as uniformly distributed across the surface as would be ideal. Figure 3
191 shows the DTM footprints over the global mosaic, illustrating their distribution and the
192 corresponding GCPs.

193 To aid in GCP selection, we rendered a terrain-shaded relief (TSR) map of each local DTM
194 (Figure 2). A separate, small control network was then developed for each TSR. At the 32 ground
locations, we identified at least three GCP features. Every image that overlapped a GCP location was interactively co-aligned, and the sample/line coordinates were recorded as a GCP image measurement. In general, the GCP that included the most overlapping images was held fixed in the bundle adjustment (i.e., not permitted to adjust), and the remaining GCPs were constrained (i.e., allowed to adjust within a tolerance). Of the GCPs in this network, 32 were fixed and 95 were constrained. The final result was a total of 127 GCPs, 792, and 217 TPs, and 3,257,452 associated image measures.

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## Image-to-Image Control

For each image in the data set (Table 1), we identified the overlapping images that shared ≥3.5% ground coverage with that image. This allowed us to apply feature-matching techniques, using the `ISIS3 findfeatures` application, to sets of overlapping images [Becker et al. 2017]. Features were detected with the Features from Accelerated Segment Test detector [Rosten and Drummond, 2005]. Then, image descriptors around each detected feature were extracted using the Scale Invariant Features Transform [Lowe, 2004]. Finally, using the image descriptors, features were matched using the "BruteForce" matcher algorithms (https://docs.opencv.org/3.4/d3/da1/classcv_1_1BFMatcher.html). We used outlier rejection techniques to eliminate mismatched or poorly matched features, including ratio [Lowe, 2004] and symmetry tests and outlier detection via RANSAC [Fischler and Bolles, 1981] in the computation of fundamental and homography matrices.

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The feature-matching process created a separate control network for each set of overlapping images. To combine the image-to-image networks into a single network, we used the `ISIS3 application cnetcombinept`, which statistically evaluates whether a pair of control points should be merged into a single control point. To establish a merge priority order for control points, we adapted the image index algorithm used by the MESSENGER Dual Imaging System team to determine their image prioritization for mosaicking [Denevi et al., 2018].

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Once an image-to-image network was established, it was combined with the GC, TP, and associated image measures from the smaller image-to-ground networks. After the networks were merged, we refined all measures within control to subpixel accuracy using the area-based
image correlation ISIS3 application *pointreg* with a constrained goodness-of-fit tolerance of 0.9 or higher [Garcia et al., 2015]. Using ISIS3's *cnetcheck*, we examined the final global network to verify that images were fully connected to avoid standalone images ("islands") that were disjointed from the network and performed an initial bundle adjustment. We removed measures with residuals >1 pixel as well as images with a poor (i.e., only covering a portion of the image) spatial distribution of tie point measures, where the residual is reprojection error measured in the focal plane. The network was checked for islands a final time prior to the final bundle adjustment.

## 232
## 233 Bundle Adjustment

The control network that we describe contains 3142 PolyCam images from five flybys, but only 2155 were selected for inclusion in the basemap. No points had fewer than three measures (meaning there were at least three overlapping images identified that contained the same feature), and >90% had four or more. The bundle adjustment, performed using the ISIS3 application *jigsaw*, converged with a root mean square (RMS; i.e., reprojection error) of image measure (TP and GCP) residuals of ~0.082 pixels. The RMS of image measure residuals for GCPs was only ~1 pixel. This equates to 5 cm or one-third of the average resolution of the local DTMs. The GCP measure residuals reflect the higher uncertainty of manual image measurement compared to automated feature matching. The manual measurement of GCPs was further complicated by the difference in resolution between the images (5 cm) and the local DTMs (15 cm), which is illustrated in Figure 2.

To assess the accuracy of the bundle adjustment solution, we computed the difference between the adjusted radius coordinate of each point triangulated in the bundle adjustment to the radius at the corresponding location in the global DTM. The mean absolute error of these differences was 47 cm. Roughly 90% of the adjusted points lie within 1 m of the global DTM. The remainder deviate from the surface up to a maximum of ~22.0 m. The highest deviations occur predominantly around the edges of large boulders and on the floors of craters (Figure 3). To estimate the spatial accuracy of the solution, we performed a separate adjustment in which 11 GCPs were constrained and the remaining 116 were treated as checkpoints and allowed to
adjust freely. We compared the adjusted checkpoint coordinates from the solution with their a
priori values. The RMS of the checkpoint coordinate discrepancies was ~30 cm, both in
latitude/longitude and in radius. Because of the variable quality of the global and local DTMs used for
control, it is unclear whether the principal source of positional error is the DTM itself or the manual
measurement of GCPs.

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<figure><img src="image_3.png" /></figure>

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Figure 2: (a) A single map-projected PolyCam image from Flyby 3. (b) A PolyCam image from Flyby 4A that overlaps with that shown in panel (a). (c) The high-resolution local DTM converted into a TSR image, showing the overlapping footprints from (a) and (b). The red points mark the GCP at the prime meridian (–28.38° latitude, 0° longitude). Panels (d), (e), and (f) show close-up views of the same GCP in each image, identifiable by the center of the crosshairs. Identifying GCP in overlapping images taken under varying conditions, and subsequently in the lower-resolution TSR, was a time-consuming manual process.
<figure><img src="image_4.png" /></figure>

Figure 3: Top: Map of the radius coordinate residuals after applying the *jigsaw* bundle adjustment. The white circles correspond to locations where GCPs were established. The white polygons represent the high-resolution DTM footprints that were available at the time of image registration. The GCP at–28.38° latitude, 0° longitude indicates the prime meridian feature, which is located on the edge of the distinctive boulder with which it overlaps. Bottom: Histogram representation of the 792,344 residual calculations. Most of the radius coordinates calculated during bundle adjustment fall within 1 m (above or below) the radius of the DTM at that point.

## Basemap Image Selection

A Geographic Information System (GIS) was critical for the selection and prioritization of images contributing to the basemap out of the larger set of images that form the control network. We used ArcMap to visualize map-projected image footprints and to run spatial queries to ensure that data with the desired geometry also provided adequate coverage (Figure 4). Image footprints and photometric angles were calculated using the ISIS3 applications *camstats* and *footprintinit* and integrated within a spatial database. Because the effects of foreshortening are more pronounced in images with high emission angles, and consequently introduce distortion when orthorectified and map-projected, the initial image selection criterion was a low
emission angle. Within the Flyby 3 dataset, images with emission angles greater than 30° at the center pixel were excluded. For Flyby 4A, images north of -70° latitude with emission angles less than 40°, and images south of -70° with emission angles less than 55°, were selected. Images with emission angles of <45° were selected from Flyby 4B. For both Flyby 4A and 4B, only images with incidence angles of <90° were used, resulting in the elimination of image data that was shadowed at polar latitudes. Flyby 3 was composed of images from an equatorial sub-spacecraft latitude and therefore had no images with prohibitively high incidence angles. The goal of these initial cutoffs was to find the images with best possible geometry for each flyby while maintaining full coverage. The cutoffs were established empirically, and more detailed choices between overlapping images were made at a later stage. Using the GIS, we also identified images from Flybys 5A and 6A to improve coverage near the poles and fill in areas shadowed in the Flyby 3, 4a, and 4b data sets. These areas are explicitly identified in the Mosaicking section of this paper.

<figure><img src="image_5.png" /><figcaption>Figure 4: Image footprints from Flybys 3 (yellow), 4A (green), and 4B (blue) after image selection and displayed in an equirectangular projection.</figcaption></figure>
306 Orthorectification and Map Projection

307 After the data were selected from the globally controlled data set, we orthorectified and
308 map-projected the images into either equirectangular or polar stereographic projections (Table
309 2). Orthorectification uses terrain and camera model information to remove several types of
310 geometric distortion, including lens distortion, oblique viewing conditions, and local surface
311 shape. It also resamples images to a uniform ground sample distance and associates each pixel
312 with body-fixed coordinates, enabling us to create map products.

313

<table><caption>314 Table 2: Bennu global basemap projection parameters.</caption>
   <thead>
    <tr>
     <td colspan="2">
      Projection Information
     </td>
    </tr>
   </thead>
   <tbody>
    <tr>
     <td>
      Equatorial Radius
     </td>
     <td>
      250m
     </td>
    </tr>
    <tr>
     <td>
      Polar Radius
     </td>
     <td>
      250m
     </td>
    </tr>
    <tr>
     <td>
      Center Longitude
     </td>
     <td>
      180.0°
     </td>
    </tr>
    <tr>
     <td>
      Center Latitude
     </td>
     <td>
      0°
     </td>
    </tr>
    <tr>
     <td>
      Latitude Type
     </td>
     <td>
      Planetocentric
     </td>
    </tr>
    <tr>
     <td>
      Longitude Direction
     </td>
     <td>
      Positive East
     </td>
    </tr>
    <tr>
     <td>
      Longitude Domain
     </td>
     <td>
      360°
     </td>
    </tr>
    <tr>
     <td>
      Ground Sample Distance
     </td>
     <td>
      0.05m
     </td>
    </tr>
   </tbody>
  </table>

315

316 When converting image coordinates to latitude and longitude, we calculated body-fixed
317 coordinates per pixel using the tessellated shape model. This conversion assigns radius values at
318 each pixel based on the intersection with an individual facet. The DTM allows for a more
319 accurate representation of the global asteroid shape and also represents local terrain, including
craters and individual boulders, with less warping. Bennu's surface includes boulders with lengths >10% of its equatorial diameter [DellaGiustina and Emery et al., 2019], hence accurate representation of these features is necessary to minimize the distortion and identify terrain occlusion during map projection.

Unlike asteroids with an elongated global shape or large surface concavities, such as (433) Eros [Bussey et al., 2001] and (25143) Itokawa [Fujiwara et al., 2006], Bennu can be intuitively represented in standard 2D map projections (e.g., equirectangular) despite having an irregular shape. Therefore, after calculating the latitude and longitude coordinates using the DTM, we projected those coordinates onto a sphere. Table 2 shows the projection information used to make the Bennu global basemap.

## Mosaicking

To create the final global basemap, we combined the images into individual mosaics for each of the three main flybys—Flybys 3, 4A, and 4B—before merging them into a single global map and adding the Flyby 5A and 6A images. Image stacking order was determined chronologically, where the first image acquired was the first image placed into the map. Any image having a center pixel with an extreme latitude (>70° or <-70°) was masked by its local incidence angle; pixels with incidence angles >= 90° were removed.

Before combining the three flyby mosaics into a single map, we normalized each mosaic to a uniform phase angle of 30°. When the flyby mosaics were created, their photometric angle backplanes were also mosaicked, such that each flyby mosaic has a corresponding phase, local incidence, and local emission angle mosaic backplane (Figure 5). The phase angle backplane was used to correct the observed reflectance to a predicted reflectance at a phase angle of 30°. We used the Minnaert phase function presented in Golish et al. [“Imaging Spectrophotometric Behavior of the Surface of Asteroid (101955) Bennu”, in review, *Icarus* 2019 545].

Disk correction, which normalizes images to uniform emission and incidence angles, was not applied. Disk correction removes the contribution of terrain from an observed scene. Because Bennu's global basemap provides a basis for geologic and hazard mapping, the surface relief in the OCAMS images was important to preserve.
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To minimize the “seams” between the different flyby campaigns and maximize the portions of the mosaic with the lowest emission angles (and therefore the smallest amount of projection distortion), the three mosaics were manually masked before they were combined. Initially, we masked each flyby mosaic according to the emission angle for each pixel that contributed to it. Any pixels in the overlapping regions with emission angles >40° were removed. Next, we manually identified a boundary of acceptable low-emission angle surfaces for each mosaic in an iterative fashion. These boundaries indicated where Flyby 3, which targeted an equatorial latitude, overlapped with Flybys 4A and 4B and had comparable, low emission angles. The flyby mosaic with the lowest emission angle was usually given mosaicking priority; however, we avoided dividing large geologic features and distinct regions, such as boulder fields and craters floors, between two mosaics (Figure 5).

<figure><img src="image_6.png" /></figure>

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<caption>Figure 5: The global mosaic showing the local emission backplanes. The seams between the three mosaics between 10 to 20° and -10 to -30° are difficult to see because we chose the boundaries where the local emission angles were similar.</caption>

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Next, we refined the mask for each mosaic by tracing individual natural feature boundaries at a much smaller scale. These included individual boulder edges, small crater rims,
and shadows, which further minimized the seams between the flybys (Figure 6). Generally, we avoided drawing a flyby boundary through the middle of a rock because such a seam would become amplified after photometric correction. However, some boulders were large enough that portions of them were only visible at a low emission angle in Flyby 3, whereas other portions were only visible from a low emission angle in Flybys 4A or 4B. This means that for some of the boulders, one face had significant distortion in one mosaic but not in the other, and vice versa (Figure 7 a,b). In these instances, the boulder was carefully divided between the two flyby mosaics to avoid creating a visually dramatic seam (Figure 7 c). Additionally, large features near the poles, and in some cases near the equator, cast large shadows and obscured the surrounding surface. In these cases, we masked out the shadows and supplemented the missing data with images from Flybys 5A and 6A. We performed the hand-tracing described in this section using Adobe Photoshop. The masks were then converted from PNG into the ISIS3 cube format and applied using the ISIS3 application *mask*. The mosaics were then combined into the final global basemap. The masks, and therefore the coverage from each flyby, can be seen in Figure 8.

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<figure><img src="image_7.png" /></figure>

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<caption>Figure 6: (a) An area through which runs the seam between Flybys 3 and 4A that was created using natural feature boundaries. (b) The same seam, but with Flyby 3 brightened to highlight where it occurs.</caption>

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<figure><img src="image_8.png" /></figure>

Figure 7: (a) A boulder (−10°, 260°) in the Flyby 3 mosaic. Significant distortion can be seen in the southern portion of the boulder. (b) The same boulder in the Flyby 4A mosaic, this time with distortion at the north face. (c) The two mosaics combined across the middle of the boulder such that the distortion is removed. This single boulder is resolved by over 900 facets in the global DTM. Therefore, placing the mosaic seam on the boulder where the local emission angles are the most similar gives us the most accurate 2D representation of the feature.

<figure><img src="image_9.png" /></figure>

Figure 8: Map of precise coverage by each flyby used for the global basemap after applying the handmade masks.
401 Bennu Basemap

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The global mosaic of Bennu is presented in an equirectangular projection in Figure 9.

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Additionally, north and south polar stereographic mosaics show the full extent of the maps from 90° to –90° latitude in Figure 10. The map can also be texture-mapped back onto the high-resolution DTM and displayed in 3D, as shown in Figure 11.

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The global basemap of Bennu reveals several geologic areas of interest. Boulders that are diverse in size, brightness, morphology, and spatial abundance dominate the surface of Bennu [DellaGiustina and Emery et al., 2019; Walsh et al., 2019]. There are several instances where the highest-relief areas of large boulders are littered with smaller boulders or cobbles. Figure 12a shows a larger boulder (diameter, ~16.7 m) that includes a shallow depression with centimeter-scale material aggregated inside. The provenance of this material is unclear—it may be from an impact on the boulder itself, or linked to the particle ejection events that have been observed on Bennu's surface (Lauretta and Hergenrother et al., 2019). Figure 12b shows a boulder (diameter, ~5.1 m) with brighter clasts embedded within a darker host matrix and is similar to putative breccias identified elsewhere on Bennu's surface [Walsh et al., 2019; DellaGiustina et al., 2019]. Figure 12c
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<figure><img src="image_10.png" /><figcaption>Figure 9: The global basemap of Bennu displayed in the equirectangular projection with the parameters given in Table 2.</figcaption></figure>

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<figure><img src="image_11.png" /><figcaption>Figure</figcaption></figure>

10: The global basemap of Bennu displayed in polar stereographic projection (Table 2). The top map shows latitudes spanning 0 to 90°, and the bottom map shows latitudes from 0 to -90°.

Both maps have latitude grid lines in 10° increments, which start from 0° at the outermost mark.
<figure><img src="image_12.png" /><figcaption>Figure 11: Bennu basemap overlaid on the high-resolution shape model.</figcaption></figure>
<figure><img src="image_13.png" /></figure>

Figure 12: Close-ups show the true resolution of the basemap. Inset (a) shows individual rocks within the concave structure of a larger boulder. Inset (b) shows a boulder with inclusions of a brighter material. Smoother, brighter boulders above a darker, hummocky boulder can be seen in inset (c), which illustrate the diversity of boulder texture and albedo. Inset (d) shows a crater with fine-grained material inside.

## Conclusion

We used OCAMS data collected during the Detailed Survey phase of the OSIRIS-REx mission to produce a global basemap of asteroid Bennu. We achieved this by registering calibrated images to both a high-resolution DTM and to each other using ISIS3 applications modified for OSIRIS-REx. We map-projected the images, mosaicked the individual flybys, and normalized the reflectance to a standard phase angle of 30°. Finally, we carefully masked the overlaps between the north, equatorial, and south mosaics using natural feature boundaries before mosaicking the flybys into a single, global basemap. The nearly seamless image mosaic spans the full range of latitudes and longitudes and represents the finest scale at which a planetary body has been mapped.
Exploration of small bodies is becoming increasing common. Our methods can be adapted by other missions that map asteroids, comets, and especially irregularly shaped bodies with rough surfaces. Although constructed for the OSIRIS-REx sample site selection process, the global basemap of Bennu includes a wealth of data for assessing the geologic processes at work on the surface of a rubble-pile asteroid.

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## Acknowledgments

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This material is based upon work supported by NASA under Contract NNM10AA11C issued through the New Frontiers Program. Funding for the Canadian authors was provided by the Canadian Space Agency. We are grateful to the entire OSIRIS-REx Team for making the encounter with Bennu possible and to Catherine Wolner for editorial guidance during the writing of this paper.

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## Data Availability

OCAMS images are available via the Planetary Data System (PDS)

(https://sbn.psi.edu/pds/resource/orex/). Data are delivered to the PDS according to the OSIRIS-REx Data Management Plan available in the OSIRIS-REx PDS archive [Crombie and Selznick, 2019]. Higher-level products such as mosaics and shape models will be available in the PDS 1 year after departure from the asteroid.

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