Thermophysical Change Detection on the Moon
with the Lunar Reconnaissance Orbiter Diviner sensor
Silvia Bucci∗
Polytechnic of Turin, Italy
silvia.bucci@polito.it

Jose Ignacio Delgado Centeno∗
University of Luxembourg
jose.delgado@uni.lu

Ben Gaffinet∗
RSS-Hydro, Luxembourg
bgaffinet@rss-hydro.lu

Ziyi Liang∗
University of Southern California
ziyilian@usc.edu

Valentin Bickel
ETH Zürich VAW
bickel@vaw.baug.ethz.ch

Ben Moseley
ETH Zürich AI Center
benjamin.moseley@ai.ethz.ch

Miguel Olivares-Mendez
University of Luxembourg
miguel.olivaresmendez@uni.lu

Abstract
The Moon is an archive of the history of the Solar System, as it has recorded andpreserved physical events that have occurred over billions of years. NASA’s Lunar
Reconnaissance Orbiter (LRO) has been studying the lunar surface for more than
13 years, and its datasets contain valuable information about the evolution of theMoon. However, the vast amount and heterogeneous nature of data collected by
LRO make the extraction of scientific insights very challenging - in the past mostanalyses relied on human review. Here, we present NEPHTHYS, an automatedsolution for discovering thermophysical changes on the surface using one of LRO’slargest datasets: the thermal data collected by its Diviner instrument. Specifically,
NEPHTHYS is able to perform systematic, efficient, and large-scale change detection of present-day impact craters on the surface. Further work could enable morecomprehensive studies of lunar surface impact flux rates and surface evolution
rates, providing critical new information for future missions.

1

Introduction

The nature, magnitude, and frequency of present-day changes of the lunar surface are of key importance to understand and reconstruct the Moon’s and the Solar System’s history, to analyze the surface
evolution of airless planetary bodies and to identify potential environmental hazards for future robotic
and crewed For the Moon, the most extensive datasets that allow for the study of the surface areprovided by NASA’s Lunar Reconnaissance Orbiter (LRO) mission, which has been operating since2009. Two of LRO’s most important instruments are its NAC (Narrow Angle Camera, Robinson
et al. (2010)), which acquires high-resolution (∼0.5 m footprint) optical images, and Diviner (Paige
et al. (2010)), which acquires intermediate-resolution (∼200 m footprint) point measurements of thesurface temperature.
∗

Equal contribution

Machine Learning and the Physical Sciences workshop, NeurIPS 2022.

Despite the abundance of these global datasets with long temporal baselines, our knowledge about
present-day changes is limited. Past work has been able to identify a small number (≲ 1000) of
such changes - predominantly fresh, small impact craters ( Speyerer et al. (2016), Watters et al.
(2022), Williams et al. (2018)), but has faced three major challenges: 1) the massive size of theexisting datasets (hundreds of terabytes) which makes human mapping impossible, 2) highly variable
illumination conditions, and 3) complex noise in the dataset which makes faint and small change
signals hard to detect. For example, the most extensive change detection study so far (Speyerer et al.
(2016)) relied on the matching of multi-temporal optical NAC images acquired at similar local times,
which limited their spatial coverage to only 6.6% of the lunar surface.
To overcome such limitations, we turn to machine learning to provide more automated, scalable
and efficient change detection, which accurately detects low signal-to-noise events. In particular,
we focus on detecting thermophysical changes within the Diviner dataset; such changes are usuallycaused by physical events such as small impacts but can have significant spatial thermal expressions
and remain visible for hundreds of thousands of years (Williams et al. (2018)). Specifically, we
focus on detecting fresh impact craters and their surrounding ejecta blankets, which have different
night-time cooling properties than the regular lunar surface, producing so-called cold spots (Williams
et al. (2018)) (Fig. 1). Fresh impact craters are believed to represent the largest source of lunar
surface change.
We introduce NEPHTHYS (New Event Perceiving High-Trust High-Yield System), which searches
for cold spots by using two different neural networks (a CNN-based binary classifier and a normalizing
flow based anomaly detector) to analyse preprocessed thermal images constructed from the Diviner
thermal point measurements. The output of the networks includes a detection confidence level toincrease their reliability. Our workflow is validated by scanning areas with known fresh impacts andthen verifying candidates by hand using NAC optical images (Williams et al. (2018)).

Figure 1: An example of impact detection with NEPHTHYS. Left: thermal Diviner mosaic (showing
average night temperature) of a scanned region (280x280 km): orange and blue dots indicate potential
candidates for fresh craters found by the binary classifier and anomaly detector, respectively. Greendot inside the bounding box indicates a known recent impact, the target of the search. Validation of
the detection: (A),(B) our preprocessed thermal image before and after the impact, (C),(D) NAC
optical image before and after the impact.

2

Methodology

Our high-level detection workflow consists of two main steps. First, we carry out preprocessing of the
raw Diviner point measurements (See Appendix A for a detailed description of the Diviner dataset).
Specifically, given an area of interest on the surface, we bin and aggregate the point measurements
into two images, representing a before and after image of the surface temperature spanning the entire
operational window of the instrument (Fig. 2). Secondly, we feed these images into two different
detection networks, which independently predict whether an impact has occurred by searching foran associated cold spot in the images. Finally, these networks can be scanned over the entire lunar
surface to provide an efficient and large-scale detection method.
22.1

Data Preprocessing Workflow

Following Moseley et al. (2020), we first sort and store all of the available Diviner point measurements
into three-dimensional “data cubes”, indexed by location (in 0.5° latitude and 0.5° longitude intervals)
and local time (6-hour intervals), for computational retrieval efficiency. We then produce one before
and one after image for each region of interest: before images are populated using the oldest available
Diviner measurements over the respective location; after images are populated using the most recent
Diviner observations over the respective location. Importantly, we currently only use night time
point measurements acquired between 22:00 and 06:00 local time to avoid the complex changes intemperature associated with daytime illumination. All data over 2009-2022 is used. Populating thebefore and after images consists of two steps: a) Data normalization and b) Binning and aggregation.
Gaussian smoothing is also required as third step before creating a difference image, used as the input
of one of our ML solutions (Section 2.3).

Figure 2: Data preprocessing workflow used by NEPHTHYS.
In Fig. 2 we show a raw temperature image (after Data cubes download). We note that heterogeneous
stripes of different temperatures can be observed. This is because some point measurements aretaken at an earlier local time (within the 22:00 - 06:00 window used) when the surface has not cooled
down completely (brighter color). The darker regions instead correspond to measurements taken at
a later local time. To fix this issue, we normalize the measurements to a reference time (precisely,
00:00 local lunar time) computed using Hayne’s model (Hayne et al. (2017)), which simulates the
background temperature variation with local time for a given latitude on the Moon. After this step,
we obtain a more uniform image (Fig. 2, after Local time normalization).
In order to produce before and after images from the normalized point measurements, we needto go through the second preprocessing step: Binning and aggregation. For each image, we bin
all the available point measurements onto 100 × 100 m grids. To create a before image we takethe measurements from the earliest year available in each bin and for the after image, we take themeasurements from the most recent year. Lastly we average the selected measurements in each bin to
get the final image pairs. To create a difference image, we subtract the after image from the before
image. We note that Diviner’s pointing accuracy includes slight errors, occasionally resulting in a
spatial misalignment of measurements, resulting in spatial noise in the difference images. Gaussian
filtering (Chung (2020)) is employed to smooth out this noise after the binning step in both images.
The last two pairs of images in Fig 2 show the effect of the Gaussian filtering.
2.2

Binary Classification

A straightforward solution to detect the presence of a fresh cold spot by analyzing the before andafter images separately is to check for the presence of a cold spot in both - if the before image does
not contain a cold spot but the after image does, then there was a recent change. We note there is adifference between old cold spots and fresh cold spots: the former refers to impacts that occurred
before 2009 (the year the LRO diviner mission launched); the latter refers to impacts that happened
after 2009 - those are actual present-day changes.
We use a Deep Neural Network (DNN) (He et al. (2016)) as a binary classifier to determine whether
an image contains a cold spot or not. Our training data is a catalog of ∼ 2000 known, old cold spots aspositive samples (taken from Williams et al. (2018)) and ∼ 2000 random points as negative samples
taken over the entire lunar surface. We can safely use random points as negative samples given thatthe probability of picking a point with an old or fresh cold spot is negligibly small (Williams et al.
3(2018)). The trained classifier can be deployed on the entire lunar surface: for each point we produce
the before and after images, use the model to classify both, and evaluate for changes.
2.3

Anomaly Detection

Ideally, if there were no changes, the before and after images of the same location should be identical.
However, minor measurement localization inaccuracies add noise (background noise). This is thereason why applying simple thresholding techniques on the difference images is often challenging forchange detection. As an alternative approach, we propose to formulate the problem as an anomaly
detection task (Hendrycks et al. (2018), Yang et al. (2021)), shown in Fig. 3. Given a difference
image, we want to assess whether the image lies within the distribution of difference images where
no fresh cold spots are present. Our solution is based on DifferNet Rudolph et al. (2021), a recent
approach in which a Normalizing Flow (NF) Rezende and Mohamed (2015) is applied to the features
extracted from a DNN to compute such an anomaly score.

Figure 3: Overview of the architecture used for anomaly detection Rudolph et al. (2021)
Let XT be the set of training data composed of the difference images of locations without fresh
cold spots (the 2000 random locations used for the BC). For each xi ∈ XT , let ti be the features
extracted from the DNN in Fig. 3, and let zi be the features extracted from the NF. Following Rudolph
et al. (2021), we maximize the likelihood of the extracted features t which are quantifiable in thelatent space Z. After a change-of-variables and the use of the negative log-likelihood (Rudolph et al.
(2021)) obtain the following loss function to train the NF:
L(ti ) =∂z∥zi ∥2− log det
2
∂t

(1)

Intuitively, we want that NF maps t as close as possible to z = 0 for all of the training examples.
During evaluation we can compute the anomaly score through the negative log-likelihoods for eachtest sample xj ∈ Xeval :τ (xj ) = E[− log pZ (zj )](2)

An image is classified as anomalous if the anomaly score is above some threshold value, Θ.

3Results and validation

To evaluate the quantitative performance of our binary classifier, we consider a validation set made of
400 images (half positive, half negative): it is able to achieve about 95% of accuracy on them. For
the anomaly detector we consider the known fresh cold spots (enhanced with augmented versions ofthem) and random points: it assigns a score 4-times higher on average for the difference images that
contain a cold spot on them. A downstream task for the validation of our approach can be seen in
Fig. 1. Here we chose an area of 10x10 degrees in latitude and longitude that contains one known
fresh impact that occurred in 2013 (Williams et al. (2018)). The two detectors identify a number offresh cold spot candidates (orange dots: binary classifier, blue dots: anomaly detector). Importantly,
both approaches were able to find the target of the experiment, the known recent impact (green
dot). We repeated the same experiment for 4 additional sites with a-priori known fresh impacts:
NEPHTHYS was able to consistently find every known fresh impact. Unfortunately, the locations ofmost existing known fresh impacts have not been made publicly available by LROC, and only these 5
were available from the Diviner team (Williams et al. (2018)) and recognizable in Diviner data.
44Conclusion, limitations and future steps

We have implemented NEPHTHYS: an ML-based approach to detect present-day thermophysicalchanges on the lunar surface. A pre-processing pipeline was implemented to convert point measurements from the Diviner instrument into images suitable for ML-driven change detection. Two
independent ML solutions were developed, which were able to efficiently scan a region of the Moonand produce a list of potential surface changes. Verifying the detected sites was one of the biggest
limitations of our approach due to the lack of known impacts and significant human effort required tocheck the candidates with NAC imagery. In future work, we would like to submit target acquisition
requests to the LROC team to further verify NEPHTHYS’ candidates. We would also like to assess
whether NEPHTHYS can detect other thermophysical changes, such as moonquakes and landslides.
Newly discovered surface changes will provide key insights into the history of the Moon and theSolar System and contribute essential information to the planning of upcoming lunar missions.

Acknowledgement
This work has been enabled by the Frontier Development Lab Program (FDL USA). FDL is acollaboration between SETI Institute and Trillium Technologies Inc., in partnership with NASA,
USGS, Google Cloud, NVIDIA, PLANET and the Luxembourg Space Agency (LSA).

5

Broader impact

The main goal of our work is enabling planetary science researchers to process and analyze NASA’s
Lunar Reconnaissance Orbiter (LRO) data in an automated manner. We believe that our work could
have a great impact in the society. Helping in having successful lunar missions, our work contributes
to the exploration of the universe whose goal is also to find other planets suitable for life. Considering
the climate change and all the other problems that our planet is facing, a backup plan becomes urgent.

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6

AppendixA

LRO Diviner dataset

The LRO Diviner instrument (Paige et al. (2010)) is a passive radiometer on board NASA’s LRO
satellite which has been gathering point measurements of the lunar surface temperature along the
ground track of the satellite over the past 13 years. Measurements are collected across nine different
wavelength channels from optical to thermal wavelengths. Over multiple orbits, LRO Diviner has
covered the entire surface of the Moon, providing multiple measurements at each surface location
taken over the full range of local times (noon over midnight to noon). All collected data points are
publicly available on NASA’s PDS 2 . We use a processed version of the raw Diviner data that waspointing-corrected and calibrated (Level 1), specifically including channels 7, 8, and 9 (covering 12.5
to 200 µm), provided by the Diviner team (Paige et al. (2010)).

B

Implementation details

Binary Classification. We build our approach over a standard ResNet-18 (He et al. (2016)) pretrainedon ImageNet (Russakovsky et al. (2015)). We train our model for 2000 epochs using batch size 32,
learning rate 0.001 and Adam optimizer. We use the standard cross-entropy loss to finetune only thelast block of the network keeping the rest frozen with the ImageNet weights. As data preprocessing
we first resize the images to 256, then we crop every image into 50x50 crops covering with 121
windows the entire image. Lastly, we resize every crop to 224 and feed the images into the network.
Anomaly Detection. We used as feature extractor AlexNet (Krizhevsky et al. (2017)) followed by
the Normalizing Flow block as proposed in DifferNet (Rudolph et al. (2021)). We update the weights
only for the Normalizing Flow block keeping the rest frozen with the ImageNet pretrained weights.
We first resize the images to 448 and then crop the image to four 224 sub images with no overlap,
then resize each sub image to 448. Considering the performances on the validation set, we set thethreshold value Θ to 0.8.
For the training of both of the architectures described in the previous section, a NVIDIA Tesla
V100 was used. The lightweight nature of the networks used made it possible to run multiple
iterations of the training for optimization purposes in a short amount of time. For inference, a similar
hardware setup will make it possible to scan specified lunar surface regions with the change detection
algorithms.

2

https://pds-geosciences.wustl.edu/missions/lro/diviner.htm

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