IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2022

1

Danish Airs and Grounds:
A Dataset for Aerial-to-Street-Level Place Recognition andLocalization
Andrea Vallone1∗ , Frederik Warburg1∗ , Hans Hansen2 , Søren Hauberg1 and Javier Civera3

Abstract—Place recognition and visual localization are
particularly challenging in wide baseline configurations. In thispaper, we contribute with the Danish Airs and Grounds (DAG)
dataset, a large collection of street-level and aerial images
targeting such cases. Its main challenge lies in the extreme
viewing-angle difference between query and reference images
with consequent changes in illumination and perspective. The
dataset is larger and more diverse than current publicly available
data, including more than 50 km of roads in urban, suburban
and rural areas. All images are associated with accurate 6-DoF
metadata that allows the benchmarking of visual localization
methods. Additionally, we validate our data by presenting theresults of a simple map-to-image re-localization baseline. that firstestimates a dense 3D reconstruction from the aerial images andthen matches query street-level images to street-level renderingsof the 3D model. The dataset can be downloaded at: https://frederikwarburg.github.io/DAG/.
Index Terms—Localization, Mapping, Data Sets for SLAM,
Deep Learning for Visual Perception, Visual Learning

I. I NTRODUCTION

E

Stimating the 6-Degrees-of-Freedom (6-DoF) camera
pose in a known scene map is a core component inmany applications such as autonomous driving, robotics, andaugmented reality. Visual localization pipelines are typicallydivided into two stages. First, place recognition methods obtaina coarse camera pose by finding images from a large databaseof registered images that are similar to a given query image.
Second, a visual localization method refines the relative pose
between the retrieved and the query images, in most cases
relying on feature extraction and matching.
Handcrafted
descriptors
have
shown
impressive
performance for both place recognition and visual localization(e.g., [1], [2]), but are limited to small changes in perspective,
illumination and scene structure. In the last decade,learning-based feature extractors and descriptors haveovercome these limitations, even for drastic appearancechanges such as day-to-night or summer-to-winter. The need
Manuscript received: Feb, 3, 2022; Revised May, 11, 2022; Accepted June,
9, 2022.
This paper was recommended for publication by Editor Cesar Cadena
Lerma upon evaluation of the Associate Editor and Reviewers’ comments.
∗ The first two authors contributed equally to the work. 1 Technical
University of Denmark,{s192327,frwa,sohau}@dtu.dk,
2 Dansk Drone Kompagni ApS, hans@dronekompagniet.dk, 3 I3A,
Universidad de Zaragoza, jcivera@unizar.es
This work was funded by the Spanish Government (grant
PGC2018-096367-B-I00), the Aragón Government (grant DGA FSE-T45
20R), research grants (15334, 42062) from VILLUM FONDEN, and fromthe European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation programme (grant agreement 757360).
Digital Object Identifier (DOI): see top of this page.

Fig. 1: Sample images from our DAG dataset illustrating
the challenge of retrieving the closest aerial image given a
street-level query, and of registering the query in the aerialreference frame.
for training data and fair benchmarking have motivated therelease of many large and challenging place recognition [3],[4], [5] and localization [6], [7], [8] datasets that focus
especially on appearance and viewpoint changes. Following
this trend, aerial mapping is a particularly interesting
application to study viewpoint invariances.
Moreover, aerial mapping has a wide range of applications.
Compared to street-view mapping, in which drivers orpedestrians have to traverse every road, aerial images provide amore scalable method for mapping large areas. The alignmentof multiple mapping sequences is simpler with airplane photos
than street-level sequences, because of the large receptive field
and overlap of aerial images. Compared to satellite photos,
airplanes provide oblique views and higher resolution thatallows for detailed mapping of building facades (e.g. see thedetailed texture on the facade of the power plant in Fig. 1).
This paper contributes to the ongoing research on visual
place recognition and localization with a challenging dataset
presenting extreme viewpoint changes. Specifically, theDanish Airs and Grounds (DAG) dataset targets visual
place recognition and localization between aerial andstreet-level images. DAG contains diverse urban and suburban
environments and is currently the largest and most diverse
dataset of its kind.
To validate the dataset, we implemented a baseline method
for aerial-to-street-level visual localization. We first create
a 3D model from aerial images from which we renderstreet-level images, thus reducing the view-angle difference
between query and database images. Similarly to [9], we find

2IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2022

that pre-trained feature descriptors are effective for visual
localization between the rendered and the query images. Our
method, however, comes at the expense of an expensive
3D reconstruction and rendering processes. We hope thatthe release of the DAG dataset will facilitate researchin direct visual localization between aerial and street-level
images without the need of rendered views, and that our
implementation will contribute as a valuable baseline.
II. R ELATED W ORK
Visual localization pipelines are typically divided into place
recognition and 6-DoF localization. In this section, we review
the main works in both stages, the most relevant datasets forvisual localization and specific works in aerial-to-street-level
localization.
A. Visual Place Recognition
Visual place recognition is often cast as an image retrievaltask, where the goal is to find images from the same place asa query image in a large database of geo-registered images.
The definition of same-place varies, but usually two places are
considered the same if they are within a certain distance radius(25 meters is a common choice). Retrieval methods are morescalable than full 6-DoF motion estimation, but only provide
a coarse localization (that of the closest database image).
Therefore, place recognition methods are often used as an
initial step to constrain the 6-DoF localization to a few images.
Classical visual place representations consist of handcrafted
local descriptors aggregated with either Bag-of-Words(BoW) [10], Fischer vectors [11] or Vectors of LocallyAggregated Descriptors (VLAD) [12]. Learning place
representations using deep networks has boosted thecapabilities and performance of place recognition. The
architectures consist of a convolutional backbone followed
by a pooling operation, such as max-pooling [13] oraverage-pooling [14]. Radenovid et al. [15] proposed a
Generalized Mean Layer (GeM) that learns the normof the pooling-operator, and thus generalizes max- andaverage-pooling. Arandjelovic et al. [16] proposed NetVLAD,
a deep architecture that also learns the VLAD clusters.
MultiViewNet [17] and Warburg et al. [5] incorporate multiple
views to improve retrieval performance. The Bayesian triplet
loss [18] mirrors the triplet loss, but allows a network to embed
images into Gaussian distributions rather than points, and thus
propagate uncertainties to image retrieval. More similar to ourwork, Sourav et al. [19] explored extreme viewpoint changes
by having query and database images from opposite directions.
B. 6-DoF Visual Localization
Methods for camera localization have traditionally beenclassified as either structure-based or regression-based [20].
Regression-based methods train a deep network to directlyregress the camera pose from an input image. Some notableapproaches are PoseNet [21], that estimates the absolute poseof a camera with respect to a scene, and the works by Laskar
et al. [22] and Balntas et al. [23], that estimate the relativepose between two cameras. However, recent evaluations (Zhouet al. [24] among others) seems to show that direct pose
regression is less accurate than the more traditional one basedon feature extraction and matching.
Structure-based methods, on the other hand, predict thepose of the camera by matching features between a 3D modeland 2D query images. Traditional handcrafted descriptors
struggle to match images taken under strongly differingviewing conditions. [25] tackle large variations in viewing
angle by rendering SIFT features from views with less extreme
view-angle difference. Modern localization methods rely on
convolutional neural networks to extract features that are morerobust to appearance and viewpoint changes. SuperPoint [26]consists of a convolutional encoder followed by two heads:
one for classifying if a pixel is an interest point, and theother to encode a feature descriptor. D2-Net [27] has asingle CNN that extracts dense features that serves both as
descriptors and detectors. LOFTR [28], on the other hand,takes a pair of images as input and via a ViT [29]-basedtransformer architecture estimates both keypoints and matches
simultaneously. Another line of research has focused onlearning local descriptors using image level supervision
only [30], [31], [32], [33]. DELF [30] learns a spatial attention
that is used to pool the feature map and can thus be optimized
similarly to retrieval networks, but via the attention mechanism
yields local features. Combining networks that predict botha coarse place descriptor and local descriptors [34], [35],[36], [37], [38] have shown to improve both efficiency androbustness.
C. Visual Localization Datasets
Many large localization datasets have been proposed inrecent years, focusing mainly on viewpoint and appearancechanges. Among the most relevant place recognition datasets
we can cite Nordland [3] with seasonal changes, Tokyo24/7 [4]with day-night changes, and MSLS [5], which is currently thelargest and most diverse place recognition dataset including
viewpoint, structural, seasonal and day-night changes. 6-DoF
datasets include ground truth poses that are typicallyobtained from SfM reconstructions or differential GPS.
Oxford Robotcar [6] traverses the same loop 100 times
during a year in varying weather and day/night conditions.
Extended CMU Seasons dataset [7], [39] is similarly recordedwith a car-mounted camera. Aachen Day-Night [7], [40]contains images from hand-held devices and focuses on
day-night changes. The recent ETH-Microsoft [8] covers
indoor environments and challenging day/night appearancechanges. All these datasets only contain street-level images
from ground vehicles or handheld devices. In contrast, our
DAG dataset contains images taken by a ground vehicle andan airplane.
D. Aerial-to-Street-Level Retrieval and Localization
Aerial-to-Street-Level registration was addressed by Shanet al. [41]. Similarly to us, they propose a view-dependentmatching process. However, they assume to know theapproximate street-level position from GPS EXIF tags, while

VALLONE et al.: DANISH AIRS AND GROUNDS

Street-level Query

West

3

South

Downward

North

East

Fig. 2: The airplane has five cameras mounted facing downwards, West, South, North and East. The figure shows images fromthe suburban environment in the Lolland sequence and the harbor environment at the Nordhavn traversal.

we run a deep place recognition model to obtain such coarse
localization. As a second difference, their 3D reconstructions
are created from the street level, which is only possible when
multiple ground-level images of the same area are available.
We show that accurate 3D models and street-level renders
can be estimated from aerial images This generalizes easily
to scenes with few street-level data.
Lin et al. [42] propose to train a place recognition networkfor direct aerial-to-street-level retrieval. They construct a
dataset that covers several large cities with both aerial andstreet level images. They train a place recognition network to
be invariant to the extreme viewpoint change between aerialand street level images. In contrast to their work, we seek tofind local correspondences to improve the coarse localization
estimate of the place recognition model.
Reducing viewpoint differences between aerial and groundimages using generative adversarial networks (GANs) was
explored in [43], [44], [45], [46]. These methods are valid
alternatives to our baseline. Note, however, that their view
syntheses come with no guarantees and might produce
low-level artifacts that affect the localization performance.
[47] is the most similar to our work, releasing a 2
kilometers-long sequence captured by a drone in Zürich and
Google Maps images from the same places. Our DAG dataset
is significantly larger, covering over 50 kilometers in morediverse urban and suburban environments. Their ground-truth
car poses come from GPS and have limited accuracy. In
contrast, our street-level images are annotated with differential
GPS and are thus more accurate.
III. T HE DANISH A IRS AND G ROUNDS (DAG) DATASETDAG contains aerial and street-level images from urban,
suburban and rural regions in Denmark1 . The airplane photos
are taken by five cameras; one facing vertically downward,
and four oblique views facing each of the world corners (East,
West, North, South). The images were recorded by The Danish
Agency for Data Supply and Efficiency in 2017 and 2019. See
Fig. 2 for examples or visit their website for an interactive look
1 The access to the data was possible thanks to the open access policy of
the Danish Government, c.f. https://dataforsyningen.dk/.

at the images2 . The airplane photos are recorded at an 150
meter altitude and with a spatial frequency between 20 − 70
meter. Their associated poses are in principle within 5 meter
precision, which is further improved by visual alignment andmulti-view reconstruction.
SkagenMotorring 3

Nordhavn

LollandRoskildevej

Fig. 3: The five DAG sequences cover a large geographicalarea in Denmark. The sequences are captured at urban,
suburban and rural regions, as highlighted with an example
image from each sequence. The four red sequences are used
for the training set and the blue sequence kept as the test set.
The street-level images are recorded with a Ladybug5+by the Danish Road Directory. Fig. 3 shows the locationof the five sequences; the Nordhavn sequences consist of
three sequences recorded in an urban harbor environment
from a boat and a car. The Motorring 3 sequence is a
suburban road around Copenhagen, the Roskildevej sequenceis in a urban environment, and both the Skagen and Lolland
sequences are from rural areas in Denmark. The recorded
sequences cover more than 50 km and have more than 11, 000
panoramic images, which we project into four perspective
2 https://skraafoto.kortforsyningen.dk

4IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2022

Aerial Images

3D Reconstruction

Street-view Renderings

Query Images

Fig. 4: Street-level-to-aerial localization pipeline. We generate a dense 3D reconstruction from the aerial images. From thisreconstruction we render a database of photo-realistic images for real-image-to-render retrieval from street-view queries.

cameras, totaling over 44, 000 images. Fig. 3 shows someexamples of street-level images from different environments.
The trajectories and the photo orientation is determined by
a combination of differential GNSS and IMU. In particular
the NovaTel IMU-FSAS 3 is used, which measures roll, pitch,heading with high precision. Following, the trajectories arerefined using signaled fixed points to achieve a precision of
approximately 5 cm.
IV. S TREET- LEVEL - TO -A ERIAL L OCALIZATION
We implemented a localization pipeline that can bedenoted as image-to-render, an intermediate category
between image-to-image and image-to-map matching(following the terminology of [48]). The extreme parallax
angle and scale change between aerial and ground-level
images renders image-to-image matching very challenging.
Estimating intermediate 3D representations and rendering
synthetic images at ground-level allows us to bridge
viewpoint challenges and leverage recent deep models forimage-to-image matching. Our experiments show that theappearance differences between real and rendered images are
not an issue when matching deep features.
Our method consists of the following steps, which are also
depicted in Fig. 4: First, we create a 3D model from theaerial images. Second, we render street-level images fromthis 3D model in a regular grid. Third, we use a place
recognition method to retrieve street-level renderings from thesame place as a given street-level query image. Fourth, we use
a structure-based localization method between the retrieved
rendered image and the query image to obtain the 6-DoF poseof the query image.
A. 3D Reconstruction and Ground-Level View Synthesis
We used the commercial software Agisoft Metashape4 togenerate dense 3D reconstructions from the aerial images.
Due to the large computational and memory footprint of the
3D models, we partitioned each sequence into sub-models
of approximately 2 kilometers. After that, we synthesize
3 https://hexagondownloads.blob.core.windows.net/public/Novatel/assets/
Documents/Papers/FSAS/FSAS.pdf
4 https://www.agisoft.com/

ground views of the 3D model in a regular grid with
5-meter separation between synthetic cameras. We synthesize
8 street-level renderings at each location at equally spaced
directions (45◦ between each other). We set the intrinsics ofthe synthetic perspective cameras as the same as the camera
used to record the query street-level images.
B. Place recognition
We use a Resnet50 followed by the GeM aggregation
layer [15] as our place recognition network. We trained the
network with the triplet loss and hard negative mining. We
found that pre-training on the MSLS [5] significantly improves
the retrieval performance. Fig. 5 shows examples of some of
the triplets presented to the network during training. Note thatthe anchor and the positive are from the same place and thenegative is from a different place. We found experimentallythat it is important that the anchor and the positive image ineach triplet are of the same type, either both synthetic or both
real images.
C. 6-DoF Visual Localization
Once the initial place is retrieved, our method proceedswith the actual 6-DoF localization, which is based on a
Perspective-n-Point (PnP) solver [49]. The goal is to find thecamera pose that, given a set of 3D points, minimizes the
reprojection error of the 2D points in the camera plane. The
peculiarity in our case is that the 3D points are calculated
by back-projecting the pixels of the rendered camera withassociated depth information, while the 2D projections are
extracted from the original picture’s corresponding pixels.
We experimented with both SIFT [50] and D2-Net [27] asfeature detectors and descriptors. We use the ratio test [50]to filter matches for SIFT, but use a cross-matching checkfor D2-Net as suggested by the authors [27]. We use
only the 1000 best matches to increase the chances for
RANSAC convergence. Once the rendered picture matches
were identified, each pixel in the rendered image was
backprojected to obtain its 3D coordinates in the world using
the depth of the 3D reconstruction. We then use a PnP solver
to obtain the 6-DoF pose between the 2D and 3D point
correspondences.

VALLONE et al.: DANISH AIRS AND GROUNDS

5

Triplet R50 (DAG)
Triplet R50 (MSLS)
Triplet R50 (MSLS + DAG)

R@1
0.68
0.35
0.80

R@5
0.78
0.48
0.90

R@10
0.82
0.55
0.92

R@20
0.87
0.64
0.95

M@5
0.63
0.29
0.77

M@10
0.60
0.27
0.74

M@20
0.56
0.24
0.71

1.0

0.8

0.8

0.7

0.6

0.6

Triplet R50 (DAG)
Triplet R50 (MSLS)
Triplet R50 (MSLS + DAG)

0.4
0.2
0.0

1

5

10

k

20

mAP

RecallTABLE I: Recall (R) and Mean Average Precision (M) at {1, 5, 10, 20}. Both methods consist of a ResNet50 followed by aGeneralized Mean (GeM) layer. Pre-training on MSLS significantly improves the coarse localization performance.

0.5
0.4
0.3
1

5

10

k

20

Fig. 6: Recall and mean average precision (mAP) at k fora Resnet50 with GeM pooling trained with the triplet loss
on DAG, on MSLS, and finally pre-trained on MSLS andfine-tuned on DAG (MSLS+DAG).

AnchorPositive

Negative

Fig. 5: Examples of training triplets. Note that in the harborenvironment, Nordhavn, the containers are not in the same
location when the car and the airplane visited the site. As
seen in the last row, the rendering that is geographically close
to the car, shows just the front of a container. These structuralchanges make visual localization challenging.
V. E XPERIMENTAL R ESULTS
A. Place Recognition
Fig. 6 and Table I show the recall@k and the mean
average precision (mAP@k), evaluated at k number of nearest
neighbors, on the test sequence Roskildevej. We use thestandard threshold of 25 meters for true positives. A Resnet50
with a GeM-layer, trained with the triplet loss (Triplet R50(DAG)) correctly retrieves the same-place database image
68% of the times (Recall@1 is 0.68) in the test set.
Pre-training the network on the very large place recognition
dataset MSLS [5], and then fine-tuning on DAG, results ina significantly improved performance (Triplet R50 (MSLS +DAG)). With this setup, the Recall@1 increases to 0.80. The
model pre-trained at MSLS without fine-tuning in our DAG
data (Triplet R50 (MSLS)) performs significantly worse than
the first two, due to the domain gap
Fig. 7 shows some qualitative examples of the network
retrievals. The network struggles in scenes with dynamic
objects and vegetation. We believe that vegetation is a

Fig. 7: Qualitative retrieval results. 1st column: query images.
2nd to 5th columns: in order, four closest renders retrieved
from the database. Green/red boundaries indicate distances
between query and retrievals smaller/greater than 25 meters.
Observe that the network learned invariances to texturaldifferences between rendered and real images. Our method
is especially challenged in areas without buildings.

particularly challenging instance of this dataset. The aerial
images and street-level images are not taken at the same time,thus trees and bushes change appearance (summer/winter).
Furthermore, one of the limitations of our localization pipelineis that the 3D reconstruction of vegetation is very coarse. As
the aerial images are not taken at the same time, changes
in the vegetation (motion caused by the wind, vegetation
growth or seasonal effects) result in a smoothing of the
3D reconstruction. Research into direct aerial to street-level
localization (without 3D reconstruction) or learning methods
that consider such changes are promising directions as they

6IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2022

can circumvent this limitation.
B. Visual Localization

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

fraction of images

fraction of images

In this section we evaluate the localization error of therelative pose between a query and the rendered image retrieved
as described in Section IV-B. We evaluate several alternatives:
1) assigning to the query the pose of the closest database image(denoted as NN), 2) using SIFT/D2-Net features and PnP as
described in Section IV-C.
Fig. 8 shows cumulative error plots (fraction of images withtranslational and rotational errors under different thresholds).
SIFT offers the worst results, due to the differences between
low-level textures in real images and rendered ones. D2-Net
performs significantly better, as it learned higher level, moresemantically meaningful description of the features, that isless dependent on specific low-level texture patterns. Our best
median errors are around 5 meters and 7 degrees, which isremarkable given the challenging nature of the data.
Note finally that D2-Net matches outperform the rotation
estimates of a simple nearest-neighbour matching, but not intranslation. We attribute this to the aggregation of the errors
involved in the visual localization estimate. The aerial image
resolution is 10 centimeters per pixel. Assuming matchingerrors over 1 pixel, parallax angles between 20◦ and 40◦
and small translation and rotation errors, they propagate
to triangulation errors over 1 meter. Such reconstruction
errors may be bigger for textureless areas, vegetation anddynamic objects, and propagate to the localization via PnP.
Our optimal RANSAC threshold is 40 pixels, which indicatesthat there exist matches with high error that also add up to
the localization error. We also observed unevenly distributed
matches. Simulations of the geometry of the problem gaveerrors of the same level as those obtained with the real data.
Fig. 9 shows several examples of D2-Net matches between
the query and database images after cross matching. Observe
how the features extracted on buildings have in general low
image errors. Matches on the road and in vegetation, on theother hand, have a coarser localization in the image.

0

5

10

15

20

translation error [m]

25

0.8
0.6
0.4

SIFT
NN
D2Net

0.2
0.0
0

5

10

15

20

rotation error [deg]25

Fig. 8: Cumulative translation and rotation errors fornearest-neighbour (NN), SIFT-based and D2-Net-based pose.

Triplet R50 (MSLS + DAG) + NN
Triplet R50 (MSLS + DAG) + SIFT
Triplet R50 (MSLS + DAG) + D2-Net

5m/5◦

10m/10◦25m/25◦

0.02
0.01
0.04

0.10
0.11
0.42

0.75
0.29
0.66

TABLE II: Ratio of queries under several pose thresholds.
Table II shows the ratio over all retrievals of those witherror under certain translation/rotation errors (e.g., 42% ofFig. 9: Inlier matches between query images and thestreet-level renderings. Since aerial images are taken at
different times, moving objects such as cars and leaves aresmoothed out or blurred. However, D2-Net is still able tofind reliable matches between the two views. The challenging
aerial-to-street-level viewpoint changes are alleviated via 3D
renderings and D2-Net produces reasonable results.
all queries have pose errors below 10m/10◦ ). Observe how
D2-Net features and PnP improve over the retrieval poses
for well conditioned images (thresholds 5m/5◦ and 10m/10◦ ).
However, pose estimates in cases with noisy matches andreconstructions increase the error with respect to retrieval(threshold 25m/25◦ ). This suggests that further research isneeded for wide baseline feature matching, and we hope our
DAG dataset is useful for this task.
VI. A DDITIONAL V ISUALIZATIONS OF THE DATAIn Figure 10, we present additional visualizations from thedataset. These images highlight again the difficulty of the
problem and the diversity of the DAG dataset, covering urban,
suburban and rural areas. As seen in the second row, the dataset
also includes seasonal and dynamic changes between the aerialand street level image.
VII. C ONCLUSIONSIn this paper, we have presented Danish Airs and Grounds(DAG), a dataset for aerial to street-level visual localization.
Our data collection is the largest, up to date, that addresses
such challenging setup. We believe there are two main aspects
that make DAG relevant for the robotics and computer vision

VALLONE et al.: DANISH AIRS AND GROUNDS(a) Aerial Images

7(b) Street-View Images(c) Street-View Renderings

Fig. 10: Sample visualizations of aerial, street-level images and street level renderings. The images illustrate the diversity ofscenes in our DAG dataset.

8IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2022

communities. Firstly, it addresses a particular case of widebaseline matching, which is one of the hardest cases for
retrieval and localization. And secondly, from a more practicalperspective, targets the relevant application case of street-level
localization in aerial maps. We present a simple map-to-image
re-localization pipeline for wide-baseline matching. In our
experiments we analyze the performance of such an approach,serving as validation and initial baseline for our dataset.
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