Scale-aware Recognition in Satellite Images under Resource Constraints
Authors: Shreelekha Revankar
Cheng Perng Phoo
Utkarsh Mall
Bharath Hariharan
Kavita Bala
Venue: ICLR 2025 Poster (2025)
Keywords: Satellite Imagery
Resolution
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A system for scale-aware recognition in satellite imagery under resource constraints

Abstract: Recognition of features in satellite imagery (forests, swimming pools, etc.) depends strongly on the spatial scale of the concept and therefore the resolution of the images. This poses two challenges:
Which resolution is best suited for recognizing a given concept, and where and when should the costlier higher-resolution (HR) imagery be acquired? 
We present a novel scheme to address these challenges by introducing three components: (1) A technique to  distill knowledge from models trained on HR imagery to recognition models that operate on imagery of lower resolution (LR), (2) a sampling strategy for HR imagery based on model disagreement, and (3) an LLM-based approach for inferring concept "scale". With these components we present a system to efficiently perform scale-aware recognition in satellite imagery, improving accuracy over single-scale inference while following budget constraints. **Our novel approach offers up to a 26.3\% improvement over entirely HR baselines, using 76.3 \% fewer HR images.** Resources are available at https://www.cs.cornell.edu/~revankar/scale_aware.

Published as a conference paper at ICLR 2025
SCALE-AWARE RECOGNITION IN SATELLITE IMAGES UNDER RESOURCE CONSTRAINTS
Shreelekha Revankar's Cheng Perng Phoo', Utkarsh Mall?, Bharath Hariharan' Kavita Bala'
Cornell University "Columbia University
ABSTRACT
Recognition of features in satellite imagery (forests, swimming pools, etc.) de- pends strongly the spatial scale of the concept and therefore the resolution of the images. This poses two challenges: Which resolution best suited for recog- nizing given concept, and where and when should the costlier higher- reseuurrsooltion (HR) imagery be acquired? We present a novel scheme to address these challenges by introducing three components: (1) technique to distill knowledge from mod- els trained on HR imagery to recognition models that operate imagery of lower resolution (LR), (2) sampling strategy for HR imagery based on model disagree- ment, and (3) an LLM- bbaaeed approach for inferring concept "scale". With these components we present a system to efficiently perform scale-aware recognition in satellite imagery, improving accuracy over single-scale inference while follow- ing budget constraints. Our novel approach offers up to a 26.3% improvement over entirely HR baselines, using 76.3% fewer HR images. Resources are available on our website
National Forest
Town
Forert
Fo)eotst
(d/Track
Forest
TraTrakk
100 HR Images
LR IRana
100 HR Images
LR Image
Figure 1: With these images we can see how concept scale is linked to spatial resolution. If we are seeking out spatially large concept like forest, lower resolutions are favored (b), as higher resolutions may lack the needed context to discern between forest (a) and park (c). At the same time while seeking out finer concepts such as sports track, certain details can only discerned well at higher resolutions (d) and are obscured at lower resolutions (e).
INTRODUCTION
The ever-increasing number of earth observation satellites (now more than 1500) offer us a unique opportunity to understand changes the planetary scale, be it tracking the ecological degradation of forests and coral reefs (Wicaksono et al. 2021 Gao et al., 2020). the destruction of cultural heritage (Tapete et al.) 2021), or economic development and well-being (Engstrom et 2022). A key to all these downstream applications is the ability to recognize accurately a broad vocabulary of concepts: major computer vision challenge.
An important aspect recognition in satellite imagery is the notion of scale; satellite image res- olution is dependent the particular satellite/sensor and is characterized by the GSD, or ground
"Coresponding Email: revankar@e co.eooeell.dd

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Published as a conference paper at ICLR 2025
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Figure 2: System overview. First, we determine which resolution best suited for the search con- cept based its scale (Sec. 3.3 Then, analyze the search area to find which regions would benefit the most from higher resolution inference (Sec. 3.5). We sample the best suited regions while staying within one of three models, a high resolution satellite model, a low resolution satellite model, and a low resolution satellite model with knowledge distilled from its high resolution counterpart (Sec. 3.4 This knowledge distilled model allows us to infer finer details using low resolution satellite imagery alone.
specified budget. Based this guidance we perform inference using
sampling distance: the distance corresponding to single pixel. For example, Sentinel-2 images are fairly low resolution (I pixel = 100 m²) (ESA 2024), while images captured by the NAIP program are fairly high resolution pixel m²) (U.S. Geological Survey 2024). The image scale matters because many concepts of interest also have characteristic physical size/scale. An olympic-sized swimming pool, typically about 50 m long, would be barely speck in Sentinel-2 images. In con- trast, lake may be hundreds of square kilometers, necessitating exceptionally large image to capture its extent terms of NAIP imagery, which current models would not be equipped to do.
The right resolution may also depend on the underlying geography. Dense urban areas with many geographical features may require high resolution data to analyze. In contrast, large swathes of uninhabited deserts probably be analyzed accurately even with low-resolution imagery.
Accuracy notwithstanding, we must also consider cost and availability. Low-rcsolution (LR) im- agery is firee, abundant, and covers the planet densely over space and time (ESA 2024| USGS Earth
Resources Observation and Science 2024). In contrast, high resolution (HR) imagery typically comes from drones or low flying satellites that commissioned needed, and therefore come
with a high cost and inconsistent temporal and spatial coverage (Planet Labs PBC 2018- U.S.
Geological Survey 2024).
Scientists in many application domains, be it ecology, archaeology, and urban planning (Wicaksono) al. 2021 Gao et al., 2020 Tapete et al. 2021 [Engstrom al. (022), today manually reason about these different aspects, namely, the scale of the concept, the nature of the underlying geography, and the cost of acquired imagery, to achieve the best recognition that they can afford with their budget. We need an approach that can automate this tradeoff and automatically identify when to acquire more expensive high resolution imagery, taking into account the cost, the scale of the concept and the geography. While there is past work on the impact of scale on satellite image recognition, much of it focused on accuracy and disregards costs (Reed et al., 2023 Mall et al. 2024) Bastani al.) 2023). Prior work that has looked at costs focuses on the geography but ignores the scale of the concept itselfuuzenn & Ermon (2020). A holistic treatment of scale to achieve accurate recognition under budget is missing.
In this paper, we address this gap by proposing:
A knowledge distillation technique which allows LR models to improve significantly in recog- by learning A novel approach that leverages the semantic understanding of LLMs to determine the scale of each concept.
nizing finer concepts
from HR.
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A new approach that determines which geographical regions require higher resolution analysis
by predicting when low and high resolution models might disagree.
A unified framework that combines these ideas to yield the most accurate retrieval results for range of concepts while adhering to strict budget constraints.
Finally, we note that while experiments are more general and can generalize to any problem where there are two or more modalities with different costs and aceuracy tradeofTs.
LR and HR satellite imagery, these techniques
We evaluate our approach with multiple recognition models (supervised and open-vocabulary) and multiple satellite modalities. We find that compared to simply using high resolution images always, our framework improves accuracy up to 13 points while reducing the number of HR images used by 5x. Our approach also significantly outperforms (by more than 25 points) other prior work that trades off between accuracy and cost. In sum, our results demonstrate that our holistic reasoning of scale leads to significantly higher accuracy with large reductions cost.
2 RELATED WORKS
2.1 MULTI-SCALE RECOGNITION IN SATELLITE IMAGERY
Remotely sensed images satellite images inherently multi-scale. Depending the sensor
heights and properties, the physical distance between two adjacent pixels in remotely sensed images (known as Ground Sample Distance/GSD) could vary from 0.3m to Ikm. These multi-scale images
provide complementary information for various applications (Wicaksono et al., 2021 Gao et al.)
2020
Tapete et al. 2021 Engstrom al. 2022).
Few prior works have explicitly investigated the effect of different scales in visual recognition in satellite imagery. For instance, |Redd et al. (2023) shows that crucial to build scale-specific representations for images with different GSD. However, they do not explicitly address the high acquisition cost of high-resolution satellite imagery. Works on image super-resolution (Shermeyer & Van Etten 2019; Kowaleczko et al. 2022 Wolters et al., 2023) seek to sidestep the problem of acquisition cost by upsampling low-resolution to their high-resolution counterparts. But as shown in Wolters et al. (2023). these models prone hallucination and would produce high-resolution imagery with limited fidelity. In this work, we consider a more realistic setup where we allow a small budget for acquiring high-resolution images during inference. This allows tap into the stronger performance of scale-aware models such as Reed et al.) (2023); Mall et al. (2024): Bastani al. 2023) while avoiding the hallucination problem in image super-resolution.
2.2 RESOURCE CONSTRAINTS IN VISUAL RECOGNITION
Various resource constraints could be considered during the development or deployment of visual recognition models. Active learning approaches (Tuia al. 2011) consider budget for acquiring annotations during development and seek to develop the best-performing models by selecting the
most informative of training examples annotate. This could be done by selecting examples
that the model is most uncertain about (Houlsby et al.) 2011] Shannon] 2001 Yoo & Kweon] 2009))
examples that are most representative of the unlabeled data (Ash al. 2019 Sener & Savarese 2017), or a combination of both (Yin et al. 2017). Prior works have also considered compute con- straints for developing and deploying visual recognition models. These works satisfy the compute constraints either through developing specialized model architectures (Tan & Le 2019) or com- pressing/pruning existing models (Hinton et al., 2015) Blalock et al. 2020). Our work considers a different type of constraint inherent to remote sensing. i.e., the acquisition cost for high-resolution images. While others have looked into optimizing high-resolution image acquisition (Uzkent & Er- mon. 2020; Meng al. 2022), provide solution to produce the performant visual recognition models under a fixed acquisition budget with semantic understanding of the concept. To the best of our knowledge, this is uncharted problem domain.
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2.3 KNOWLEDGE DISTILLATIOW/LEARRING WITH PRIVILEGED INFORMATION
To recognize fine-grained concepts in LR images, we construct query model by distilling the log- its generated by another model that is trained on HR imagery. This approach bears resemblance to knowledge distillation (Hinton et al. 2015) Borup & Andersen [021] Cho & Hariharan, 2019)
Park al., 2019 Fukuda et al. 2017; Borup et al. 2023). which seeks compress a large-scale teacher model by training a compute-clficient student model to mimic the output of the teacher model. Different from conventional knowledge distillation, our query student model and teacher model operate on different inputs. Specifically, our query model operates on LR imagery, whereas our teacher model operates on HR imagery. Our approach can also be viewed as an instance of learning with privileged information (Vapnik & Vashist 2009, Pechyony & Vapnik 2010 Vapnik jet al. 2013), in which additional information that is available during test time (e.g., high-resolution satellite imagery) is used for training model. This paradigm has enabled the development of better- performing models in various applications, such as autonomous driving (Chen et al.] 2019). image super-resolution (Lee et al. 2020p, and on. In remote sensing, prior works (Kumar et al., [021] [i et al. 2022) have attempted to use additional sensor data as privileged information for improv- ing land-cover classification. Our work leverages high-resolution satellite image data as privileged information for improving recognition in low-resolution imagery: a different scenario.
METHODOLOGY
3.1 PROBLEM SETUP
We are interested in defining framework for optimizing accuracy/cost tradeoffs when performing recognition in satellite imagery. This framework should reason about not only the scale of the concept being recognized but also the underlying geography of each location.
We concretize the problem as follows.
Setup. We frame our problem as retrieval task: given a concept (c.g.. "tennis courts") and large set of locations (e.g.. the state of New York), wish identify which of these locations have the concept.
For each location, we have one LR image and a set of HR images that tile the same area. The size of this set, K, depends on the difference in GSD between the two modalities. For example, if LR images come from Sentinel-2 (GSD=10m) and HR images come from NAIP (GSD=Im), K would be 100.
We assume that we have special purpose multi-label classification models for each resolution, de- noted by M HR and M M respectively. Given any concept and any location i, we can use these models to score the presence of this concept at that location. For LR imagery, this is straightforward:
(i) [M
For HR imagery, we simply take the maximum over model scores for all the images that location:
H (i) MR
The max here is used because we want s HR (i) to be high (denoting the presence of the concept) any of the K HR images indicate the presence of the concept. We call these scores LR scores and HR scores respectively.
As discussed in the introduction, HR imagery is expensive. We assume budget in terms of the maximum number of HR images we can acquire, B. Our goal is, given a concept e and a set of locations I, retrieve all locations i I that have concept while staying within the HR budget.
Training / hyperparameter validation. To allow for algorithms to train any models or validate hyperparameters, we assume access to fixed set of locations Itrain where we have already acquired (a) images from both resolutions, and (b) annotations for the presence/absence of a set "seen" concepts Cseen For fairness, these locations Our set of evaluation concepts are also kept distinct ("unseen" classes); however, we also report performance on the seen classes, A full list of the concepts, seen and unseen, is in Appendix A.
distinct from the locations used for evaluation.
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3.2 SYSTEM OVERVIEW
Our overall system to tackle this problem is shown in Figure 2 and has three steps: Given search concept, our system uses an LLM-basee approach to predict which resolution it best suited towards 3.4). If the search concept was found to be better suited to HR imagery, we would ask the user for a budget and utilize our Model Disagreement based sampling technique to optimize the budget in selecting locations for HR imagery (Sec. (.5). We describe how this technique can be performed using LR imagery alone via a Knowledge-Distie Distillis LR model and how such a model allows us to perform improved inference with LR imagery alone well (Sec. 3.4
3.3 INFERRING CONCEPT "SCALE" USING LLMS
The question whether HR imagery is needed or not depends on the concept. For some concepts, the low-resolution scores s g.. are better, while for others, the HR scores s f2 are accurate. This is, unfortunately, difficult to predict without some background knowledge about the concept. To capture this background knowledge, we propose to leverage large language models (L.LMs).
Unfortunately, while LLMs have generally learned various kinds of background knowledge, it is unlikely that they have seen reasoned about the different modalities of satellite imagery. As such, while the LLM might know that a lake is bigger than a swimming pool, this may not be enough to figure out which of the two modalities better.
We address this knowledge gap with in-context learning (Dong et al. 2022). Concretely, we first use the available training locations Itrain and the annotations therein to evaluate both the HR scores and the LR scores for each concept in Cseen_ Thus, for each of these seen concepts, we know which modality yields higher accuracy. We then give these seen concepts, as well the corresponding modality, as in-context examples to the LLM and ask the LLM to infer the right modality for all other (unseen) concepts. The prompts to the LLM can found in Sec. A.2 the appendix.
3.4 KNOWLEDGE DISTILLATION FROM HR TO LR
Suppose we know apriori that a concept is better detected in HR imagery. Given the limited ac- quisition budget of B, we do not have full coverage of the locations in I. Thus we need model that consumes LR images and approximates the predictions made by M To build such model M M.D. we minimize the MSE between the model's prediction and the corresponding HR scores:
ΣΣ CECREen
[[M 80 (1)
By minimizing the loss, we essentially distill the HR knowledge (Hinton et al. 2015) the M MR into model that consumes LR images M.R. KD- This formulation also bears resemblance to learning with privileged information (Vapnik al. 2015). In this case, when training the model M use privileged HR information to guide the MER
KD to detect smaller concepts at LR imagery.
3.5 ACQUIRING HR IMAGERY BASED ON MODEL DISAGREEMENT
When detecting a small concept, LR might not be perfect since an LR image the test location It might not have enough visual signatures. In this case, we would have to leverage our budget of B to acquire HR imagery and run predictions using HH simple criterion to select a location to acquire HR imagery is by looking at the disagreement between the LR scores and HR scores. The disagreement / can thus be defined as:
5(1)
Σ CECAReM
1s HR - 5.
A location I with higher 5(l) is more likely to have concepts that " could detect where M could not (or vice versa). However, to compute ó we would have to already possess HR imagery over the whole region, which is exactly what want avoid acquiring. Instead to approximate
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5, leverage MRD as stand in for M and compute the following disagreement criterion for ranking various locations:
8((1)
E CEC
[[MKB(T(") (()
(5)
3.6 SCALE-AWARE RECOGNITION UNDER RESOURCE CONSTRAINTS
Given concept our LLM determines which resolution is best suited (See. 3.3 If the best suited resolution LR, we perform inference using the LR model and all LR imagery only. If the best suited resolution is HR, ask the user for their HR budget. Then perform model disagreement using LR imagery alone and the LR and KD models using eq.5) With these disagreement scores and the user budget, the HR model for these locations. The retrieval results for the remaining area, that it is preferable to HR for, but cannot be fit in the budget, is scored using the KD model on LR imagery. The end result is accurate retrieval where HR imagery is used sparingly, only when it is really needed.
then sample the top locations, obtain HR images and perform inference using
EXPERIMENTS AND RESULTS
We evaluate the effectiveness of our scale-aware concept recognition approach by assessing the impact of knowledge distillation, model-disagreement-e beaee acquisition, and LLM-based scale in- ference. We first evaluate our whole system then the three components individually.
4.1 EXPERIMENTAL SETUP
4.1.1 PRE-TRAINED MODELS
We perform experiments with two sets of low-resolution and high-resolution models.
GRAFT is a zero-shot vision-language model that can be used for performing open- ppen-vocabulary recognition satellite imagery (Mall al., 2024), similar to CLIP (Radford al.) 2021) We use the released model for Sentinel-2 and NATP and train our own model for the NICHT basemaps. The model architecture is ViT-B-16.
We finetune fullly-uperrissed ResNet-50 pre-trained on ImageNet on both LR (Sentinel-2) and HR (NAIP) data with Open Street Maps annotations on our training set.
4.1.2 BENCIIMARK
For experiments, require aligned LR and HR imagery fairly compare the performance of LR and HR models. However, this is not required in our problem setup or approach. We curate the following two benchmarks. Both benchmarks use Sentinel-2 as the low-resolution modality, but differ in the high-resolution modality:
Sentinel-2/NAIP.. In this benchmark. we use HR imagery (GSD=Im) captured by the National Agriculture Imagery Program (U.S. Geological Survey 2024). This program only captures data for the US so this benchmark is restricted the US. Here, one Sentinel-2 image corresponds to 100 NAIP images. The cost of acquiring similar resolution imagery ranges from $1.00-$6.00 per square km (Geocento) 2023).
We created a training dataset and validation dataset using images from the following states and regions: Arkansas, Delaware, Idaho, Maine, Rhode Island, Wyoming, and US Virgin Islands. These datasets comprised of 45,885 Sentinel-2 images for LR, and 4,588,500 NAIP images for HR in the training dataset, and 4.938 and 493,800 images respectively for the validation dataset. Our testing imagery is comprised of images from D.C., Puerto Rico, and Hawaii. The test dataset is comprised of 5,015 Sentinel-2 and 505,10 100 NAIP images. We chose these locations as they represent diverse of demographic and climatic regions within the US.
Sentinel-2NICFLL Our second benchmark leverages the NICFI Satellite Data Program Basemaps for Tropical Forest Monitoring (Planet Team 2024) which provides HR imagery (GSD=55) for

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regions of Central/South America, Africa, and Asia. Similar imagery has prices ranging from $0.60 S2.0 per square km (Geocento) 2023). Here one Sentinel-2 image corresponds to 4 NICFI images.
All data was sourced from Google Earth Engine (Gorelick et al. 2017). To minimize temporal discrepancies, LR images were collected within the same month as their HR counterparts. Follow- ing Mall al. (2024) use use (OpenStreetMap contributors (2024) to obtain ground truth annotations for 40 concepts (listed in Appendix[A.I] All data and download scripts will be publicly released.
All classes seen for supervised techniques, while all classes unseen for zero-shot baselines. We reserve 30 of these classes as seen classes and the other 10 are unseen classes when evaluating the knowledge-distilled zero-shot techniques.
4.1.3 METRICS
Since the scope of this method retrieval, we evaluate our results with mAP@k averaged over all classes. We also look at the amount of HR imagery utilized, well as runtime in seconds to perform inference on the imagery. The runtime is calculated on the images determined by each technique; for LR models our test set has 5,015 images, and for our HR models the test set has 501 1,0,50 images. Techniques utilizing HR sampling methods, such as our own or Patchdrop (Uzkent Ermon 2020) combination of these two datasets. All inference is performed using batch size of 32 single Nvidia RTX A6000 GPU. For our experiments we set a budget of 1000 locations to acquire HR imagery, with each location covering roughly 5 sq. km. We report our results in sq. km., can compare to other techniques that do not sample by location.
4.2 RESULTS
We examine our method by testing individual components. We first evaluate the entire system (Sec. 4.2.1). p. We then evaluate our LR Knowledge Distillation Models in Sec. 4.2.2 After that, we examine our model disagreement scheme for selecting modalities in Sec. 4.2.3) Finally, we assess the usage of LLMs for search concept scale determination (Sec. 4.2.4).
4.2.1 OVER SYSTEM PERFORMANCE
We assess the performance of our entire system (Table 2 by comparing the following baselines:
HR: Simply using the HR data and HR models for all locations.
LR: Only using LR data and LR model.
KD: Only using LR data, but with our KD model.
model dis.: Using our model disagreement approach to sample locations for HR imagery, but
without any LLM-based inference of whether the concept needs HR data.
LR + LLM: Using an LLM to decide which concepts need HR data, but then acquiring HR
images for all locations and using the LR model for other concepts.
KD LIM: Using an LLM to decide which concepts need HR data, but then acquiring HR
images for all locations and using the KD model for other concepts.
Patchdrop: Using Patchdrop (Uzkent & Ermon 2020) to determine HR sample locations.
Ours full: Using full system, from L.LM determine the best suited resolution, with
model disagreement technique to sample HR imagery with a constrained budget, and using our
KD model perform inference for HR-suited concepts in out-of-budget area. Which would be the same as KD + LLM + model dis.
nl. sampling: Using sampling strategies put forth in IS-count Meng et al. 2022 (via-night lights) and compare it to our own model disagreement technique.
We find that overall system performs the best on both classes of pre-trained models, outper- forming the baseline of using all HR data for all locations. With 26.3% and 24.6% improvement on zero-shot technique GRAFT, and 6% and 8.3% improvement supervised techniques in mAp and mAp20 respectively using only 23.6% of the HR images.
system handle trievals for multiple concepts at once. If all of the concepts are best suited for LR, inference using the LR model occurs at most once. If some concepts are better suited for HR imagery then inference
We see system perform more efficiently with regard to runtime

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Seen Classes
Unseen Classes
# HHR images
Inf. Time
Model
Data
mApilo
mAp40
mAp
mApa
km.
GRAFT HR
HR
0.501
0.313
0.341
0.574
25.163
1539
0.530
25,163
1575
GRAFT KD LLM!
LR,IR
0.557
0.576
0.541
0.574
25,163
1570
GRAFTLR
LR
0.482
0.507
0.379
0.471
0
17
GRAFT KD
LR
0.534
0.559
0,490
0.503
0
17
RemixeCT Liu Lin
2024
1.R
0441
0.493
0.413
0,440
0
17
CLIP RSIC Aruboman
2021
LR
0.393
0.441
0.311
0,303
0
17
OpenCI
Chen Cham et alr 21145
1.R 0.481
0.505
0.392
0,447
0
16
GRAFT LK m. samphngg Meng (222
LR,IR
0.379
0.354
0.440
0,492
5,954
325
GRAFT KD - model dis."
LR.HR
0.621
0.628
0.461
0.494
5,954
340
(Ours full)?
LR.HR
0.633
0.639
0.564
5,954
372
Table 1: Full system performance under various settings for retrieval under budget for zero-shot techniques.:TTechnigge TTchhiiquss utilizing our contributions. *; Classes seen during knowledge distillation, for all other zero-shot baselines all classes are unseen. For models using both HR and LR data, assign a budget of 1000 locations (~~ 5 sq. kms each). Our full system performs much better than the baselines with improved HR data constraints.
mApicu
mApal!
images sq.
Supervised HR
HR
0.695
0.735
25,163
1585
Supervised LR + L.M!
LR.HR
0.689
0.731
25,163
1610
Supervised +LM
25,163
Supervised LR
LR
0.451
0.473
0
14
Supervised KD1
0.606
Resnet 50 (Multi Res)
LR
0.421
0.447
0
21
SatMAF et
0.388
Cross Scate MAT Tang et al.
2021
LR 0.412
0.422
0
91
Supervised
samplin Mene 2022
Supervised PaachdropUUzkeli Uzkell & Emaon 4020
LR.HR 0.445
0.470
6,776
360
Supervised model
LR.HR
0.791
5,954
Supervised (Ours full)
LR.IR
0.736
0.796
5,954
338
Table 2:
Full system performance of various fully-supervised techniques for retrieval under budget. Techniques utilizing our contributions. For models using both HR and LR data, we as- sign budget of 1000 locations (~ 5 sq. kms each). Our full system performs much better than the baselines with improved HR data budget constraints.
happens at most twice using LR imagery and at most once using HR imagery. So irrespective of the number of concepts, can retrieve multiple concepts in about the same time.
4.2.2 RECOGNITION IN Low RESOLUTION
We compare the performance of several models for multi-label classification in Tables 3 and M on 40 classes (as listed in the appendix [A.]) using LR imagery alone. We see improvements in per- formance through our knowledge distillation process for both zero-shot and fully-supervised tech- niques. For each corresponding training data type and model architecture, also show the perfor- mance of the HR model (in gray).
We also compare zero-shot KD Model with other zero-shot baselines CLIP-RSICD (Arutiunian et al. 2021) and RemoteCLIP (Liu et al. Models with other fully supervised baselines ScaleMAE (Reed et al. 2023), SatMAE (Cong al., 2022]. and Cross-Scale MAE (Tang et al.] (202)) as well as a Resnet- fine-tuned on imagery of multiple resolutions (Multi-Res) in Table
2024) in Table 3] We compare our fully supervised KD
While the performance of our KD models reaches closer to that of HR models there still is a gap (e.g., more than 5 percentage points in mAP). This suggests that knowledge distillation from HR data alone is not enough to build better recognition systems. Therefore, a framework like ours that works on multiple resolutions needed, since not all concepts are favored by HR imagery.
4.2.3 MODEL DISAGREEMENT FOR MODALITY SELECTION
Following the methodology in Sec. 3.5 we describe how the disagreement between HR and LR models can act as good indicator for which locations would benefit from HR imagery. We found that correlation coefficient between the disagreement scores of the HR (NAIP) vs KD with the LR model is 0.9322, which signifies strong positive correlation. Similarly, the correlation coefficient
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Images Min Disogreement between models
Images from Min to Max Disegreement between LR and KD models
Figure 3: Images ranked according to disagreement between LR and HR model (top) and the LR and KD model (bottom). Both rankings are similar, with a correlation coefficient of 0.9322, even though the latter only uses LR images.
Budget 1000 High Resolution
Precision @ 1000 for Verious Dudgets of llih Resolution Samples
Our Modality Selection
0.42
0.475
Barsdmm Modaliy Selection
Confidence Eased Modality Selection
High Recol thar Only
0.40
@ Samples
0:38
@e
0.350
0.32
Modality
Based High Resolution Only
0.325
0.0
Low Resolution Only
Budget High Resolution Samples
Figure 4: Performance when using our model disagreement-basse sampling strategy. Our approach consistently yields higher precision across all budgets and across all values of K.
between the disagreement scores of the HR (NICFI) vs KD with the LR model is 0.998, which also significs strong positive correlation.
Figure visualizes images ranked by degree disagreement between the LR and HR model (top) and between the LR and KD model (bottom). We see that both approaches yield similar rankings demonstrating that the KD model can act a stand for the HR model, so that we may calculate the disagreement without requiring HR imagery.
We use this model disagreement along with the KD and HR model to show how well we perform retrieval under HR-data budget. Figures 4 illustrate the improvement in retrieval with HR data sampling technique. The figure on the left shows precision @ K at fixed HR data budget (1000 locations) and the figure the right shows precision 1000 when varying the data budget for the GRAFT model on unseen categories. Our HR data sampling strategy is significantly better than randomly sampling locations or model uncertainty- based methods. This also shows that sampling HR images leads to improvement over just using LR-data with KD models.
We compare the performance of our sampling technique against the sampling technique presented in (Meng et al. (2022), in which night lights are used as an indicator to sample more densely. The results of this sampling technique are included Table 2
4.2.4 CONCEPT SCALE INFERENCE
We compare our LLM- approach for inferring the best modality per concept with other base- line approaches. For each approach, evaluate its accuracy in terms of determining the correct modality. These baselines include either always choosing the HR modality and always choosing LR modality, selecting between the based the average of each concept, provided via

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Published as a conference paper at ICLR 2025
Model
mAP
mAp
Seen Classes
Unseen Classes
Model
mAp
mAp4)
mApico
mApal!
Sentinel 2NAIP
Sentinel-2/NAIP
Supervised HR
0.695
0.735
Supervised (Multi-Res)
0.421
GRAFT HR
0.501
0.513
0.541
0.574
Scale MAE
0.472
0.493
GRAFT LR
0.482
0.507
0.379
0.471
SaMMA
0.351
RemoteCLIP
0.461
0.493
0.413
0.440
Cross Scale MAE
0.412
0.422
ELIP-RSICD
0.393
0.441
0.311
0.303
Supervised LR
OpenCLIP
0.481
0.505
0,392
0,447
Supervised KD
0.570
0.6466
GRAFT KD
0.534
0.559
0.490
0.503
Sentinel-2/NICT
Supervised HR
0.699
0,850
GRAFT HR
0.338
0.412
0.424
0.473
Supervised LR
0.659
0.801
GRAFT LR
0.202
0.204
0.311
0.339
Supervised KD
0.681
0.837
GRAFT KD
0.300
0.369
0.414
0.425
Table 4: Performance of the supervised mod- els for concept recognition at low resolu- tion. Our knowledge-distlledd models per- form better over all classes of concepts (cor- responding HR models in grey; references included Tab. 2.
Table 3: Performance of the unsupervised models for recognition low resolution. Our knowledge- distilled models perform better on both seen and unseen concepts (corresponding HR models in grey; references included in Tab. T
OpenStreetMaps (OSM). Our LLM approach (tested using several 100% accuracy when determining the correct modality our set of concepts. We test on both seen and unseen, demonstrating the ability of easily being extended to far more concepts.
models) achieves
Both the OSM average area approach and choosing only HR imagery were correct 90% of the time whereas selecting solely LR imagery was correct only 10% of the time. This result interesting as the LLM outperformed using the average area of the concepts (OSM), suggesting that it is not just the size of the concept that is an important consideration, but also a semantic understanding of the concept and its features. We include tabulated version of these results in the appendix A..
Additional Experiments. We include the following additional experiments in the Appendices to further exhibit the effectivvness of our approach. We demonstrate the impact of different budgets, ranging from 100 locations to 1000 in A.4 Additionally, we compare our KD model with the HR model on Super-Resolution images to calculate model disagreement scores in A.5
Limitations. While our technique works well to cover all concepts regardless of scale, it performs in setting in which satellite imagery is split into two groups, low and high resolution. However, in reality, resolution is not discrete; recognizing exactly which resolution is best suited to a concept is something we have not yet determined.
We also utilize OSM data to train and evaluate our models. We acknowledge that this data contains noise, due to its crowd-sourced nature. Unfortunately, there are many sources for fine-grained labeling of satellite imagery. Therefore, we follow past work in using OSM for training and evalu- ation (Mall et al. 2024 Bastani et al. 2023 Moreover other standard sources of labeled satellite imagery such as Microsoft's 's building footprint dataset work with similar noise levels (Bing Maps) 2018). Additionally, to show robustness to this noise, we performed an experiment using the GRAFT I. model, wherein we constructed bootstrap samples by sub-sampling within our original test set 1000 times. The results show that the 90% confidence interval is within 2.43% of the reported mAP.
Our technique also does not allow for the segmentation of concepts, i.e., segmenting specific regions within lower-resolution images that are worth looking into at a higher resolution. We leave the exploration of this direction to future work.
5 CONCLUSION
Conclusions. We introduce a new approach to scale-aware recognition in satellite imagery under resource constraints. Our approach allows one to accurately detect various concepts using fixed budget HR imagery, outperforming entirely HR baselines by more than 26% mAP in zero-shot techniques, and more than 8% mAP in supervised techniques using 5x fewer HR images.
Broader Impacts. Our system offers a cost-efficient way to perform recognition in satellite imagery by maximizing HR imagery usage while cutting costs. This can have significant positive impact a wide range of scientists, anthropologists, archaeologists, NGOs, and human rights organizations, who operate limited budgets, but greatly benefit by the of satellite imagery in their work.
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Published as a conference paper at ICLR 2025
Acknowledgments We would like acknowledge Chia- Kao for his help in revising the abstract. This work was partly funded by the NSF IIS 2403015, NSF IIS 2403016, and USDA NIFA 2023-67021-39829.
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Reviews:
[{"review_id": "J4cIICBZF4", "reviewer": "Reviewer_nAgQ", "summary": "To provide a holistic treatment of scale to achieve accurate recognition under a budget, the paper tries to give solutions to which resolution is the best and where and when  the costlier higher-resolution (HR)imagery should be acquired. The main contributions are: (1)knowledge distillation is adopted to allow Low resolution models to improve significantly in  recognizing finer concepts by learning from High resolution ones. (2) Semantic understanding of LLM is used to determine the  scale of each concept.(3) A sampling strategy for HR imagery based on model disagreement is proposed. The process is frameworked as remote sensing image retrieval task. Experiments show some promising success for the proposed idea.\nThe idea is very novel and interesting.", "questions": "1.Quote\" A location l with higher δ(l) is more likely to have concepts that MHR could detect where MLR  could not (or vice versa).\" Since it is the absolute value, it is difficult to understand.\n2. How is the LLM trained? What's the confidence for the determination of LLM?\n3. Can it be understood as that the low resolution images taken as large as possible because the cost constraints is not imposed?\n4. Quote\"...Remote CLIP(Liuetal.,2024) in Table 3.\" But the table is not the detection result.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. A novel idea of scale determination is proposed for targer recognition using reasonable resolution remote sensing images.\n2. Low resolution detection model using distillation strategy is taken as the replacement of high resolution detection model.\n3. Two benchmark datasets are constructed for research.", "weaknesses": "1. The budget constraints seems actually impose no impact on the final result.\n2. The processes such as distillation, concept scale LLM training are not provided the details.\n3. The retrieval process has no systematic description to include all the subprocesses.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tsT8hKgTjD", "reviewer": "Reviewer_JoCu", "summary": "This paper introduced a new approach to scale-aware recognition in satellite imagery under resource constraints. The proposed system leverages LLMs and knowledge distillation technique. The proposed system outperformed the baselines  more than 26% mAP in zero-shot techniques, and more than 8% mAP in supervised techniques using 5× fewer HR images.", "questions": "The system is based on many ideal assumptions. Whether deployed online or offline, it will encounter foreseeable issues such as data storage and retrieval, online search, and download stability. Additionally, the overall process time and reliability are concerns. Although I like the idea, there seems to be a large gap between the proposed method and practical application.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper presents a novel framework to perform the tradeoff between cost and accuracy. \n2. The topic is interesting and valuable. \n3. The writing is clear. \n4. It is good that the authors plan to release their data and download scripts.", "weaknesses": "1. It is unclear how the system handles noisy labels using OpenStreetMap annotations. \n2. Can the proposed system retrieve multiple targets simultaneously? \n3. How to deal with the time discrepancy between high- and low-resolution images, which can lead to disagreements between the LR and HR models. Did the authors consider the impact of this issue? \n4. No mention of runtime in Table 3.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xQEcFlbyKy", "reviewer": "Reviewer_Kyd3", "summary": "This paper addresses the challenge of feature recognition in satellite imagery, focusing on the optimal use of image resolution to balance accuracy and resource constraints. The proposed system introduces three key components: LLM-based scale inference, knowledge distillation, model disagreement-based sampling. The proposed approach has been demonstrated to improve recognition accuracy while reducing the need for HR imagery.", "questions": "The paper distills a model from paired HR and LR images to predict whether HR images are necessary based on prediction differences. Why not directly train a super-resolution model on paired HR-LR images, use it to generate HR images for any LR input, and then perform recognition on both the generated HR and original LR images? If the recognition results differ significantly, HR images could be invoked. What are the advantages of the proposed, more complex method over this approach?\n\nImpact of resolution ratio on the proposed method is suggested to be discussed. Does the resolution ratio between high and low-resolution images affect the performance of the proposed method? Could you please provide some analysis or experiments to explore how this ratio influences the model’s efficiency and accuracy?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "This paper focuses on a highly interesting problem with significant industrial application potential. \n\nThe proposed knowledge distillation method and model disagreement-based sampling strategy are both innovative and effective. The distillation enhances the performance of LR models, while the sampling strategy efficiently balances the trade-off between cost and accuracy.", "weaknesses": "The approach heavily relies on the examples provided in the prompt for the LLM. The model’s understanding of the relationship between manually defined categories and image resolutions is largely based on these examples. This dependence raises concerns about the generalizability of the method to niche or less common satellite imagery concepts.\n\nThe paper does not include experiments comparing the proposed method with techniques that generate HR images via super-resolution, then combine these images with LR data for recognition. Such comparisons, focusing on both computational efficiency and accuracy, would provide a more comprehensive evaluation of the proposed approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C9fUz6WZ9Z", "reviewer": "Reviewer_CazH", "summary": "A scene classification task under resource-constrained conditions is proposed, combining image retrieval and scene classification. Knowledge distillation techniques and an active learning-like sample selection strategy are used to choose HR samples, and an LLM is employed to determine the scale of concepts.", "questions": "1. Does Equation 5 require knowledge of all the information for all seen categories at a specific location to be computed? Why are the HR score and LR score related to location? Does this limit the model to only locations that appeared in the training set? How would the model handle unknown locations?\n\n2. Is using LLM-based in-context learning to determine the scale reliable? Has the accuracy been tested on unseen categories in the validation set? Since the context is determined by the HR and LR scores, why not directly train a network using the HR and LR scores? It is well known that LLMs are very resource-intensive, and using an LLM just to determine the scale seems like overkill. Furthermore, it contradicts the premise of resource efficiency—if one cannot afford HR images, they likely don't have the computational resources to run an LLM either.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The authors address the real-world issue where high-resolution (HR) remote sensing images are paid, while low-resolution (LR) images are free, by proposing a framework that effectively improves scene classification performance under limited funding. This framework can be used to analyze which high-resolution images are more beneficial to purchase, helping researchers make informed decisions,", "weaknesses": "1. The description of the LLM section is not very clear. The authors did not specify which LLM was used, how the hyperparameters were set, or provide comparative experiments on the performance of different LLMs. This weakens the contribution of the LLM. It seems that the authors are merely using it as a tool for invocation.\n\n2. In Section 4.2.3, the discussion of Figure 3, which uses only five images to demonstrate ranking similarity, is not convincing. The authors need to provide a quantitative analysis to support this argument.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)