# Technical report on direct geolocalization from remote sensing S1 images

Adrien Chan-Hon-Tong

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## To cite this version:

Adrien Chan-Hon-Tong. Technical report on direct geolocalization from remote sensing S1 images. 2026. hal-05447254

HAL Id: hal-05447254

https://hal.science/hal-05447254v1

Preprint submitted on 7 Jan 2026

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# Technical report on direct geolocalization from remote sensing S1 images

Adrien Chan-Hon-Tong

January 7, 2026

## Abstract

This small technical report highlights that coarse direct position prediction from image is somehow possible given current deep network efficiency. 80% localisation bin accuracy is achieved across a 400km x 300km corridor quantified on a 10km x 10km grid.

## 1 Context

Despite not being fully closed, geolocalization based on image registration on a reference data is an efficient framework: there is still a large tradeoff between performance and computation speed and/or there can be issue when reference data is very different than sensor data despite (despite recent transformer-based deep models like MINIMA dataset [6], matchanything [3] or mapglue [8] have strongly advanced on this issue). But, image registration is used daily and/or worldwide for a tremendous number of applications.

Now, the starting point of this technical report is to remark that image registration refines rather than predicts position: from a functional point of view, it takes sensor in-board image, initial localization, and external reference data, then, it matches in-board image with the mirror datum in the reference data around the initial localization, eventually producing an improved localization.

As registration algorithm does not support direct matching of the sensor image onto the whole reference data, without the initial localization, one can only cut the reference data into many datum and matches each datum to the sensor image, largely increasing computational time and with the drawback of dealing with potential many successful matchings.

Even, universal features like alphaearth [1] requires to loop around all datum to find the best match of a given feature vector (despite potentially lowering from one order of magnitude the cost of the matching process).

Thus, it could be interesting to have direct prediction of the position from a sensor image only (no initial position and external data implicitly encoded in the model weight like in a nerf framework [5]).

Currently, this idea is not new: PlaNet [7] or FabMap [2] already consider it. However, those papers sample image rather on landmark (ground based
images shared by people) than along a typical platform trajectory which can
encountered locations with only low signal. Precisely, PlaNet considers single
image taken from the ground around somehow landmarks, so it is dealing with a
strong signal over noise image distribution. FabMap is more related to spatially
uniform sampling, yet, FabMap explicitly tackles loop closing using key points
rather than trying to deal with weak signal areas. Coarsely speaking, distin-
guishing between different unique buildings is very different than the ability to
combined weak information like a road like this near a lake like this into a forest
means you are here.

Thus, this paper considers the idea of direct quantized location (like PlaNet)
from remote sensing image sampled uniformly on a mission area, and, reports
that one can hope a relevant performance (despite obviously lower performance
than for registration methods - and the need of 1 model per mission).

Importantly, this method may be seen not as the main component of a visual
geo-localization but rather like a safety net or a primer: the idea is to provide a
coarse absolute localization which can then be refined by common registration
methods. For example, if a platform has to do a maneuver to exit a threat
while being in GPS denied area, it may eventually lead to a situation where the
distance to the latest valid position is very large as accelerometers are known to
drift a lot during strong maneuvers (and those maneuvers may prevent sensor
in particular sar sensor which requires long exposure duration).

2 Experiment

## 2.1 Dataset

To evaluate the feasibility of this approach, a spatio-temporal dataset of S1
mosaic images (see appendix) covering a corridor of 400 km x 300 km has been
collected through copernicus hub.

A dataloader allows to sample spatially uniformly pairs of crop from the S2
(simulating in-board sensor image) and the related position.

The temporal depth is about four small temporal interval in different months.
Train/test split is performed with 3 months to train a model and the last month
to evaluate the performance. This way, images used for evaluation have no part
seen during training.

Let stress that using disjoint images for testing is obviously required for
semantic task like image classification, but, here it may be more disputable.
Here, there is no concept to learn from the data as the goal is not really to
learn something but rather to memorize/overfit a mapping between image crop
(simulating sensor image) and related quantified location bin of this crop. As a
corollary, there is absolutely no way to generalize to data outside the selected
corridor.

In some way, we assume to have the virtual ability to train on all the data
from the distribution (corresponding to all the images from the selected area).
Yet, sensor image will never be exactly the same image as one of the crops used
during training despite being captured from the same physical location.

So, the model has both to learn invariance to mutable visual pattern arising
from conditions of image formation and transient state of the location to retain
only a visual watermarking of area seen in the image, then, brutally memorizing
the mapping between such location watermarking and quantified geographic
coordinates (so embedding is to be learned, but, mapping only to be overfit /
memorized).

## 2.2 Objective

The goal of the model is to predict the location of a given image. However,
predicting the exact coordinate in a regression framework seems impossible as
the problem is highly not smooth without possibility to know what are the
coordinates of a crop before having see an other crop of the same/near location.

So, a more relevant way is to cast this problem as a image classification
problem with classes being geographic bins. This creates a tradeoff between a
number of classes and the size of the geographical bins.

In this experiment, 10km x 10km bins are considered resulting in an apparent
1200 classes classification problem (but with class having no semantic meaning).

Such bin size is a bit too large for initializing a classical registration process
but going bellow is not trivial in this experiment. Future works should focus on
improving the accuracy of the bin.

## 2.3 Deep model

The 4 first groups of layers of a tiny ConvNext [4] is considered followed by a
simple 192 x 1020 linear as model.

S1 crops are preprocessed with histogram normalization to look like RGB
image in order to initialize the network with Imagenet weights (yet, all weights
are finetuned during training with Adam).

Importantly, the batch size is required to be very large (2048) for converging
compared to standard image classification or semantic segmentation. This is a
puzzling fact with no clear explanation at this point. Probably, a small batch
size tends to lead to forgetting, as there is no underlying semantic (the model
needs to learn what is different and not what is common). A behavior which
seems to be compensated by very large batch allowing to remind the model that
there is many things not to forget. Future works should investigate this point
further.

## 2.4 Result

As a result, the model reaches slightly more than 80% of geographical bin ac-
curacy i.e. sampling a location uniformly in a 400km x 300km area, the model
is able to say in which cell is the location 80% of the time on a grid with cell
size 10km x 10km.
# 3 Conclusion

This short technical report that despite structural limitation (the need of 1 model per mission and lower performance compared to registration), it is possible to have a correct performance in direct geographic prediction (without having to refine an initial guess).

# Acknowledgement

Thank to ESA/copernicus for opening S1 images to the community.

# References

[1] Christopher F Brown, Michal R Kazmierski, Valerie J Pasquarella, William J Rucklidge, Masha Samsikova, Chenhui Zhang, Evan Shelhamer, Estefania Lahera, Olivia Wiles, Simon Ilyushchenko, et al. Alphaearth foundations: An embedding field model for accurate and efficient global mapping from sparse label data. *arXiv preprint arXiv:2507.22291*, 2025.

[2] Mark Cummins and Paul Newman. Fab-map: Probabilistic localization and mapping in the space of appearance. *The International journal of robotics research*, 27(6):647–665, 2008.

[3] Xingyi He, Hao Yu, Sida Peng, Dongli Tan, Zehong Shen, Hujun Bao, and Xiaowei Zhou. Matchanything: Universal cross-modality image matching with large-scale pre-training. *arXiv preprint arXiv:2501.07556*, 2025.

[4] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pages 11976–11986, 2022.

[5] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. *Communications of the ACM*, 65(1):99–106, 2021.

[6] Jiangwei Ren, Xingyu Jiang, Zizhuo Li, Dingkang Liang, Xin Zhou, and Xiang Bai. Minima: Modality invariant image matching. In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pages 23059–23068, 2025.

[7] Tobias Weyand, Ilya Kostrikov, and James Philbin. Planet-photo geolocation with convolutional neural networks. In *European conference on computer vision*, pages 37–55. Springer, 2016.
[8] Peihao Wu, Yongxiang Yao, Wenfei Zhang, Dong Wei, Yi Wan, Yansheng Li, and Yongjun Zhang. Mapglue: Multimodal remote sensing image matching. arXiv preprint arXiv:2503.16185, 2025.

Precise list of S1 images considered

Sentinel-1_IW_mosaic_2025_M02_31TCL_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TEL_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TDM_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TCM_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TEM_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TDN_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TCN_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TEN_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31UDP_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31TDL_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31UCP_0.0.zip
Sentinel-1_IW_mosaic_2025_M02_31UEP_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TCL_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TDM_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TEL_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TCM_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TDN_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TEM_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TCN_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TEN_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31TDL_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31UEP_0.0.zip
Sentinel-1_IW_mosaic_2025_M04_31UCP_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TCL_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TDM_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TEL_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TCM_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TDN_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TEM_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TCN_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TEN_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31TDL_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31UEP_0.0.zip
Sentinel-1_IW_mosaic_2025_M06_31UCP_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TCL_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TDM_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TEM_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TCM_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TDN_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TEN_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TCN_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TEL_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31UCP_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31TDL_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31UEP_0.0.zip
Sentinel-1_IW_mosaic_2025_M09_31UDP_0.0.zip