Towards Optimization for Large-scale Earth
Observation Missions from a Global Perspective
Yaning Guo1,3 , Jing Chen1,3 , Zili Meng1,3 , Mingwei Xu1,2,3

1 Institute for Network Sciences and Cyberspace, 2 Department of Computer Science and Technology, Tsinghua University

3 Beijing National Research Center for Information Science and Technology (BNRist)

gyn17@tsinghua.org.cn,j-chen16@tsinghua.org.cn,zilim@ieee.org,xumw@tsinghua.edu.cn

CCS CONCEPTS• Networks → Network dynamics; Network design principles.
ACM Reference Format:
Yaning Guo, Jing Chen, Zili Meng, Mingwei Xu. 2021. Towards
Optimization for Large-scale Earth Observation Missions from a
Global Perspective. In 5th Asia-Pacific Workshop on Networking(APNet 2021) (APNet ’21), June 24–25, 2021, Shenzhen, China, China.
ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3469393.
3469674

1Figure 1: Imbalance between satellites and missions

INTRODUCTION

Earth observation is crucial for many real-world applications. With recent advances in nanosatellite technologies
that reduce the manufacturing and launching cost of monolithic satellites by ∼10,000× [2], industrial giants are launching thousands of low-Earth orbit (LEO) nanosatellites toform mega-constellations over the past years. These megaconstellations have great potential in stimulating advances inEarth observation because low orbits can take high-resolution
images and the large constellation scale enables the satellites
to travel above an observation spot at a higher frequency [5].
Yet, it is challenging to deliver the large volume (i.e., multiGbps [1]) of data with the limited downlink bandwidth.
Therefore, many recent efforts [1, 2] try to offload somecomputation to satellites and pre-process the observed data
to save bandwidth by transmitting a smaller amount of data.
Such a concept is also known as orbital edge computing.
However, with the increasing need for Earth observation,
naively matching the local nearby satellites to missions could
Supported by National Key R&D Program of China (2018YFB1800302) andNSFC (92038302 and 61832013). Mingwei Xu is the corresponding author.
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https://doi.org/10.1145/3469393.3469674

lead to sub-optimal resource utilization and mission incompletion. The main reason is the imbalance of the geographicalmatching relationship between satellites and the observation spots of missions. Most existing constellations [4] aredeployed in a grid pattern with fixed orbital parameters (asmarked by black lines for orbits and yellow dots for satellites in Figure 1). But observation spots are usually set up
unevenly according to population density (e.g., the darknessof red shades in Figure 1). Therefore, in populated regions,limited satellites and numerous missions lead to mission
incompletion, whereas in some rural regions, satellite bandwidth resources are wasted. Moreover, new missions fromdifferent spots are continuously submitted, which might also
follow a similar distribution of existing missions, making
bandwidth resources even scarcer. These circumstances prevent us from fully leveraging satellite resources to complete
more missions. Furthermore, because many applications areplanned to be deployed on satellites to occupy bandwidth [1],the performance will be further exacerbated.
Our observation is that we need to optimize Earth observation from a global perspective. In global optimization, resources are allocated to the most needed missions. Moreover,global optimization also enables the collaboration among
multiple satellites to coordinate the resources and complete
more missions.
Therefore, we build a model to optimize the transmission
globally to serve more observation missions (i.e., maximize
mission completion rate). However, the optimization meetsseveral challenges. First, it is challenging to coordinate theoptimization with the existing mechanisms of visual tasks.
For example, DDS [3] propose to send low-resolution images

APNet ’21, June 24–25, 2021, Shenzhen, China, China

first for localization and high-resolution in those region-ofinterest for further analysis. Therefore, we model the bandwidth consumption for different types of requirements (lowresolution or high-resolution). Second, because visual tasks
are usually completed with heavy-weight neural networks, it
is hard to ensure the inference accuracy. In response, we approximate accuracy with the linear regression of bandwidth
based on [3]. Finally, with the increasing scale of observation
missions, reducing the complexity of the model is necessary.
Hence, we would always check the correspondence between
satellites and missions to prune unnecessary variables.
Our preliminary simulation demonstrates a performance
improvement of up to 61.5% over the local optimization.

2DESIGN: LP FORMULATION

We establish a model for global transmission and apply linear
programming (LP) to find the optimal solution.
Inputs:• h: average area of the target in a low-quality image• Wi∗ : bandwidth constraint for communication.
Outputs:• X i j : binary variable indicating whether to transmit theimage taken by satellite i for observation spot j. Here,we actively trim the output variables and filter out thenon-corresponding combination of i and j.
• lr i j : low resolution, continuous variable from 0 to 1.
Constraints:• Bandwidth: To prevent a large amount of imaging data
exceeding the limited bandwidth, DDS [3] first sends lowquality images and uses high quality only to resend feedback regions. We model the bandwidth consumption inthis way. hr = 1 means high-resolution, size is for one rawimage, T is the transmission time between one satellite
and the ground, M is the number of observation spots.
ÍM∗
j X i j lr i j × size + hr × h × size ≤ Wi T , ∀satellite i(1)• Latency: There are low-latency requirements in militaryor emergency rescue. We mainly control the neural network completion time. tdds is the time for DNN network
to process a raw image and t is the time limit less than T.
ÍM(2)
j X i j tdds × lr i j + hr × h ≤ t, ∀satellite i• Accuracy: We must ensure the inference accuracy of theDNN network for each observation spot. According to [3],we use bandwidth to approximately express accuracy. y j
indicates whether
the mission is completed.
Ísat enum∗0Íisat enum bandwidthi j < A∗yj =(3)1bandwidthi j ≥ A
iNote that we use the DDS framework here but our method
could comply with other visual methods.
ÍOptimization goal: max iM y j , maximize the sum of completed missions means achieve as many missions as possible.

Y. Guo et al.

(a) Two orbits and high bandwidth(b) Two orbits and low bandwidth(c) Four orbits and high bandwidth (d) Four orbits and low bandwidth

Figure 2: Mission completion rate performance
Real-time setup:
To cope with real-time requests from different spots, Wi∗
will be updated after each round of optimization. All parameters are continuously updated to adjust the model.

3PRELIMINARY EVALUATION

We use Starlink parameters to simulate Earth observationand compare global (our method) and local optimization on
the mission completion rate. We also evaluate the ability ofour method to handle real-time missions and denote thisscheme as global + real-time requests in Figure 2.
As shown in Figure 2, our method (denoted as global) has
obvious advantages (i.e., up to 61.5% improvement in mission
completion rate) compared with local baseline (denoted aslocal), verifying the importance of resource coordination.
As the number of observation spots increases, local baseline’s performance declines rapidly while global has minor
degradation, showing stronger scalability. If real-time missions are considered, our method has a small reduction in theperformance compared to the original situation. Moreover,both global and local show improvement with more satellites (more affluent resources), but our method still has up to55.5% improvement. It indicates that blindly launching moresatellites is inefficient because the utilization of resources
is the real bottleneck. In the future, we will complete ourtheory and complexity analysis for our method.

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