Mapping and Monitoring Airports with Sentinel 1 and 2 Data

Urban Geospatial Mapping for the SCRAMJET Business Networking Tool

Nuno Duro Santos

, Gil Gonçalves

and Pedro Coutinho

Bluecover Technologies, Lisboa, Portugal

University of Coimbra & INESC-Coimbra, Coimbra, Portugal

WATERDOG mobile Lda., Porto, Portugal

Keywords: Earth Observation, Satellites, Open Data, SCRAMJET, Urban Mapping, Spatial Resolution.

Abstract: SCRAMJET is an online tool that allows business travellers to connect and plan to meet in any of the

airports included in their trip. To successfully deliver, SCRAMJET needs accurate and up-to-date

worldwide airport mapping information. This paper describes an assessment of the use of Earth Observation

(EO) products, in particular the Sentinel program, for improving airport mapping and monitoring its

changes. The first step is to verify the data availability of Sentinel-1 and Sentinel-2 at a global scale, and

then evaluate its adequacy for airport mapping. For monitoring airport changes, the analysis tested

multispectral change detection methods and interferometry processing techniques. The main conclusion was

that the acquisition frequency of both Sentinels is a great benefit to assure up-to-date information at a global

scale. The recommended approach for a target of 200 airports is to do the airport mapping, assisted by

Sentinels data for validation and improvements, and monitoring changes by integrating a Sentinel-2 change

detection chain (using NIR/SWIR bands), in parallel with OpenstreetMap change detection processing.

1 INTRODUCTION

SCRAMJET is a web and mobile product to connect

business travellers at airports that is being developed

by WATERDOG at ESA BIC Portugal. One of the

key assets of the tool is to maintain reliable, updated

and accurate airport maps to ensure travellers can

agree on a meeting point while planning their trip

and, once physically at the airport, find each other.

The maps comprise both indoor and outdoor

features, including the buildings’ morphology, gates

identification and Points of Interest (shops, toilets,

etc…) as depicted in figure.

Figure 1: Outdoor and indoor mapping needs.

The typical usage scenarios of the maps are:

• The user knows his gate and the gate of the

person to meet and uses the map to choose the

meeting place, for example Gate 21 or a coffee

shop POI ;

• The user lands and a location-based tool

running on his phone provides rough indoor

guidance visually identifying the place to go.

SCRAMJET will have its own airport map

information and the research presented in this paper

is crucial for two development and maintenance

needs:

• Airport Mapping: the initial geographical

information of all airports is obtained from

OpenstreetMap (OSM) and Google Maps is

used for validation. Nonetheless, many airports

have incomplete or outdated mapping data on

these platforms that needs to be validated and

improved.

• Monitoring the Airport Changes: airports

may be subject to works, renovations and

extensions that need to be detected.

The available literature on automatic airport

mapping and monitoring from remote sensing image

data is very scarce. First, previous works on

automatic airport mapping mainly focused on the

runways detection as they are the primary

characteristic of an airport. Wang et al. (2013) used

a Hough transform to judge whether an airport exists

Duro Santos, N., Gonçalves, G. and Coutinho, P.

Mapping and Monitoring Airports with Sentinel 1 and 2 Data.

DOI: 10.5220/0006674000500058

In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 50-58

ISBN: 978-989-758-294-3

in a Very High Resolution (VHR) image. Then a

scale invariant feature transform in conjunction with

a hierarchical discriminant regression tree was

employed to detect the airport area. Aytekin et al.

(2013) used a texture-based runway detection

algorithm that uses the Adaboost machine learning

package for identifying 32x32 pixel image tiles as

runway or non-runway. Second, for automatically

monitoring the airport changes, Digital Change

Detection algorithms that provide binary land cover

“change/no-change” information, can be used

(Jensen, 2015). In fact, by automatically detecting

the spatial regions within a bi-temporal image pair

where meaningful change is likely to have occurred,

a human operator (or another process) can then

analyse the changes using his/her knowledge.

ESA’s Sentinel missions are providing us with

reliable and timely open data on land, ocean and

atmosphere with high spatial and temporal

resolutions for state-of-the-art research activities and

services, e.g., natural resources management and

urban land cover mapping (Malenovský et al.,

2012). In this context, synergetic use of Sentinel 1/2

data has been used for urban land cover mapping

and change detection (Ban et al., 2017; Haas and

Ban, 2017). Although the potential of Sentinel 1/2

data has been highlighted in the above works, the

effective use of this data in the context of mapping

and monitoring airports need to be assessed.

This study aims to confirm the needs and verify

how Earth Observation satellites, in particular the

latest Sentinels satellites, can be used to assure the

best up-to-date outdoor mapping for an initial target

of 200 world airports. The work assesses the

temporal and spatial suitability of Sentinels (or other

EO data) and defines a service chain design for

airport mapping and monitoring changes.

2 EO DATA AVAILABILTY

The first step of the analysis was to confirm the

temporal availability of EO data at a global scale, by

defining a timeframe for validation on a set of

worldwide airports.

The selected timeframe was the latest two

months before the study started, from 1st December

2016 to 31st January 2017, while nine airports were

chosen, representative of different geographical

regions from USA, Europe and Asia.

During this period, two Sentinel-1 (S1) and one

Sentinel-2 (S2) were operational. The Sentinel-1

(synthetic-aperture radar) was operating with S1A

and S1B satellites, while the Sentinel-2

(multispectral) had only the S2A satellite active

(S2B was launched just on 7 March 2017).

The data procurement results of Sentinel-1 (S1) and

Sentinel-2 (S2) on these airports are presented in the

table.

Table 1: Sentinels data availability.

Sentinel-2 Sentinel-1

Europe

Lisbon

S2A 2016-12-19

2017-01-19 (S1A

IW VV-VH)

München

None (dense cloud

coverage)

2017-01-25 (S1A

IW VV- VH)

Istanbul

S2A 2017-02-02

2017-01-14 (S1A

IW VV-VH)

Malaga *

S2A 2016-12-20

2014-11-27 (S1A

SM HH-HV)

USA

Atlanta

S2A 2016-11-28

2017-01-06 (S1A

IW VV-VH)

NYC/JFK

S2A 2016-12-04

2017-01-12 (S1A

IW VV-VH)

Miami

S2A 2017-01-06

2017-01-01 (S1A

IW VV-VH)

Asia

Ben Gurion

S2A 2017-02-10

2017-01-04 (S1A

IW_ VV-VH)

AbuDhabi

S2A 2016-12-25

2017-01-07 (S1A

IW_VV)

Shanghai

S2A 2017-01-29

20170122 (S1A IW

VV-VH)

S2A has visible data from almost all airports,

including the 4 relevant bands for this study with 10

m spatial resolution: B2, B3, B4 and B8.

Figure 2: S2A True colour composition.

S1A and S1B were also capturing data in all

airports but using different modes. The main

operational mode for land is Interferometric Wide

(IW) High Resolution, typically using single or dual

polarization, with a spatial resolution up to 25 m.

The best resolution mode is Stripmap (SM) Full

Resolution, with a spatial resolution up to 10 m, that

is used only on request, typically on extraordinary

events, such as emergency management. Both

acquisition modes are available in the SLC product

format, needed for interferometry applications, and

GRD product format that is geo-referenced from

SLC.

Mapping and Monitoring Airports with Sentinel 1 and 2 Data

The acquisitions in IW mode were widely

available for all nine airports selected. Malaga*

airport was the only aerodrome found, acquired in

Stripmap mode Full Resolution and thus was added

to the baseline.

Figure 3: S1A IW VH-VV and SM HH-HV RGD

compositions.

2.1 Satellite Open Data Availability

Considering the technical specifications (namely the

spatial and temporal resolutions) of the SCRAMJET

two different open satellite data products have been

identified as the most useful: Sentinel-1 and

Sentinel-2 data. Concerning the Sentinel-2 data, it

was found that:

• Temporal frequency of Sentinel2 is fine. During

the selected timeframe S2A captured in average

1 good quality image per month, and there are

good images available in 90% of the airports.

• S2B was launched on 7 March 2017 and will

increase temporal availability.

• It may be difficult to capture images during

winter season in some airports (e.g. Munich,

Atlanta) due to high dense cloud coverage.

• Airports on the intersection of granules or tiles

need to have a special handling, such as JFK

that is right on the intersection of 4 granules.

Regarding the Sentinel 1 data, it was found that:

• IW acquisitions are available for all the 9

airports in dual polarization VV-VH excepting

in Abu Dhabi that acquisitions are done in

single VV polarization.

• Very few Stripmap (SM) Full Resolution

images are available at the archive. The ones

found were acquired from special zones, namely

the Strait of Gibraltar and a region of Germany.

• Almost all acquisitions are available in both

SLC and GRD product formats.

Other open satellite data procurement, in particular

Landsat8, was dropped since the first results pointed

that S2 has better spatial resolution.

3 AIRPORT MAPPING

The adequacy of the available data to meet the

mapping requirements was performed focusing on

spatial and spectral resolution and on the quality of

OSM data. The initial analysis covered only

Sentinels but it was later extended to analyse

commercial solutions. The three airports (Lisbon,

Istanbul, Abu Dhabi), used as analysis baseline,

were thus extended to Malaga and Malaysia in order

to address relevant data found on these areas.

3.1 Mapping with Sentinel-2

Three airports were selected for study from the

initial nine: Lisbon, Istanbul and Abdu Dhabi. The

approach was to build RBG composites with the

better resolution bands, layered with existing Points

of Interests from OpenstreetMap.

In the Lisbon airport, the S2A image was

composed with OSM data (Fig. 4), resulting on the

following findings:

• The visibility is slightly blurred. It is hard to

identify planes and gates.

• Many gates are mapped in OpenstreetMap (20

"aeroway"=>"gate, 1 "aeroway"=>"helipad")

• Infrared composition in Lisbon during winter,

(with more intense the grass) may be an

advantage to identify airport morphology

Figure 4: Lisbon S2A true colour composition with OSM.

Regarding the Istanbul airport, the S2A image

depicted on Fig. 5 highlights that:

• Although it has good visibility, additional

support photos and maps need to be used for

mapping

• The gates identified by red polygons are not

available on OSM

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

Figure 5: Istanbul S2 true colour composition with OSM.

The Abu Dhabi airport (27th in Asia) was

selected, not being as busy as Dubai International

Airport (3rd in Asia). The S2A image composition

on Figure 6 concludes that:

• It has very good visibility: parked airplanes the

new gates under construction are visible

• 41 gates are mapped in OSM (new gates were

not yet available in OSM)

Figure 6: Abu Dhabi S2 true colour composition with

OSM.

3.2 Mapping with Sentinel-1

Two study areas were analyzed: the Lisbon airport

using images acquired in the default Interferometric

Wide mode High Resolution and the Malaga airport

acquired in Stripmap mode Full Resolution. After

performing the geo-corrections of both S1A GRD

products, a RGB composite was produced with two

polarization bands (refer to Malaga RGB composite

on Fig 7).

Figure 7: Malaga S1A SM HH-HV RGB compositions.

The analysis concluded that:

• Both modes allow a good identification of

building areas and runways. It allows to easily

identify that Google Maps was showing an

outdated image of Malaga airport, with a single

runway, acquired before expansion on June

2012.

• S1A Sripmap FR (Malaga) has a more

appropriate spatial resolution than S1A IW HR

(Lisbon).

3.3 Mapping with Non-Open Eo Data

Considering that Sentinels may not have enough

spatial resolution for the needs, alternative

commercial satellite data with better resolution was

analysed. The project identified two very high-

resolution solutions from Pléiades (0.5-m) and

Deimos-2 (1m-4m) with competitive prices. An

example of Langkawi Airport at Malaysia using

Pléiades from 2017 is provided in Figure 8.

Figure 8: Malaysia Pleiades true colour composition.

The analysis concluded that:

• Pansharpened images with 50 cm resolution and

4 bands offers excellent details of the airport,

allowing recognition of the planes types

Mapping and Monitoring Airports with Sentinel 1 and 2 Data

• Temporal acquisition is not as flexible as

Sentinels at cost-effective prices

3.4 Mapping Sources Analysis

The analysis highlighted that there is no single

solution for all sites as presented in the table.

Table 2: Analysis of the airport mapping sources.

S2 S1 Pleiades OSM

Lisbon

Blurred

Low

resolution

(IW)

N/P 20 gates

Istanbul

Good

visibility

N/P N/P No gates

Abu

Dhabi

Very good

visibility

N/P N/P 41 gates

Malaga N/P

Good

resolution

(SM)

N/P N/P

Malaysia N/P N/P

V High

resolution

N/P

The best and relevant mapping sources depend

on the particularities of each site. The conclusions

per mapping source are presented below:

• Sentinel-2 images may be used to support

morphology and gates visual mapping and

validation. The spatial resolution may be just on

the limit. Acquisitions with good visibility are

fine for gates but hardly recognize airplanes.

• Sentinel-1 can also support the identification of

runways and build-up areas, but GRD IW High

Resolution products have a spatial resolution

less than 25 m.

• Some cases may need commercial very high-

resolution images.

• OSM does not offer a complete mapping

solution on all cases analysed. Not all airports

have gates identified in OSM.

• Additional support photos and maps may be

used for morphology and gates. Note that

Airport Buildings do not have clear boundaries.

They are often confused with surrounding

buildings (hotels, etc…).

The mapping conclusion is that the acquisition

frequency of Sentinel is a great benefit and the

solution shall definitely be based in a combination of

different sources.

4 MONITORING THE AIRPORT

CHANGES

The usage of the change detection methods could be

useful to trigger airport morphology changes. The

two typical binary land cover changes that we want

to detect are:

• Urban to Demolition

• Null Soil/Vacant Land/Demolition to Urban

In this context, two detections approaches were

evaluated:

a) Change detections with Sentinel-2: detect

abrupt changes using image pairs, before and after

the event, and a reasonable number of pixels

(between 9=3x3 a 25=5x5).

b) InSAR with Sentinel-1: use an InSAR

technique to detect surface deformations upon

analysis of the phase difference between two radar

signals acquired from the same area at different

times. The usage of Advanced InSAR for the

identification of hotspots subsidence at airports was

kept in standby at this stage. Although it may

resolve millimetre-scale movements of

infrastructure, the usage of multiple images was

considered having high costs (storage and

computation).

4.1 Study Area

The area selected for testing was the Rio de Janeiro

airport, which was renewed for the 2016 Olympic

Games. The works started in 2014 and finished in

Abril 2016.

Figure 9: Google Maps historical data of Rio de Janeiro

airport.

The gates were extended with a new area and

more car parks were constructed.

4.2 Detecting Changes with Sentinel-2

The pair of S2 images selected for testing were the

first cloud-free image available from this airport

(Fig. 10): one image was acquired in 2015 during

renewal, and the other in 2016 after renewal.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

Figure 10: Sentinel-2 images used in change detection.

For change detection using a pair of images three

main categories of methods could be used:

• Simple Detection: use Mean Difference, Ratio

Of Means or Root Mean Square Differences of

the relevant bands (typically visible and near

infrared bands 2, 3, 4 and 8)

• Normalized index change detections: produce

normalized indicators related to built-in areas

(using S2 bands) and compare them. The most

relevant index is the Difference Built-up Index

(NDBI) applied in Landsat TM with SWIR1

and NIR bands (Zha et al., 2003).

• Post Classification Comparison: make

supervised classification of the pairs and

compare results (e.g. land cover comparison,

Built-up Areas comparison)

In this paper, only the first two categories were

analysed and presented hereafter. Post Classification

was abandoned since it was considered more

relevant with global and regional scales (world,

country, regions) rather than local scales such as the

airports gates details.

Ratio of Means Detection with NIR Band

This analysis started by using simple detectors. The

ratio of means was firstly used with NIR band (B8)

from Sentinel-2.





, 





2015



, 



2016



, 



The achieved results were quite acceptable, allowing

to easily identify the new gates area and the

reconstructed car park that was not initially

identified during google maps inspection.

Although this detector was successfully applied

on Rio airport study area but it needs to be bounded

and normalized to be applied widely on other

airports:





, 



1min

2015



, 



2016



, 



2016



, 



2015



, 



Figure 11: Change detection with NIR.

Ratio of Means Detection with SWIR Band

The second approach was the ratio of means using

the SWIR band (B11) from Sentinel-2. Although

this band has lower spatial resolution, the results

achieved are also quite acceptable.

Figure 12: Change detection with SWIR.

Note that this detector needs also an

improvement in order to be bounded and

normalized.

Root Mean Square Differences Detection with 4

Bands

The third detector was the Root Mean Square

Differences computed with the visible and near

infrared bands (B2, B3, B4 and B8). Because the

obtained results are unclear, it was dropped.

NDBI Index Detection

The last multispectral detector was the Normalized

Difference Built Index (NDBI), which is referred in

the change detection literature as a promising

method (Jensen, 2015; Zha et al., 2003). For its

Mapping and Monitoring Airports with Sentinel 1 and 2 Data

usage with Sentinel-2 imagery, the S2 SWIR and

NIR bands were used as follows:





, 





_11



, 



_8,

_11



, 



_8



, 



Nonetheless, the change detection results with NDBI

2015 and NDBI 2016 obtained confusing results.

Figure 13: Change detection result with NDBI 2015-2016.

The change detection conclusion was that simple

detectors with NIR and SWIR bands could solved

the problem on this study area. The usage of these

bands shall be further verified and confirmed on

other airports. The usage of spectral unmixing

techniques at pixel level with significant changes on

land cover could be an alternative approach for a

future analysis to fine-tune the detections.

4.3 Interferometry Processing with

Sentinel-1

For the analysis of the interferometry processing, a

pair of Sentinel-1 images from 2015 and 2016 were

used. Both images were acquired in IW mode with

dual polarization VV-VH (Fig. 14).

Figure 14: IW product pair used in change detection.

The GRD products were used to produce a RGB

colour-composite from VH and VV polarization

images. The composites allowed to identify the

extended gates in 2016 (Fig 15).

Figure 15: RGB composition of S1A IW VV-VH pair.

The SLC products were used for the

interferometry processing. The pair of products were

captured in IW mode with three sub-swaths (IW1,

IW2 and IW3) using Terrain Observation with

Progressive Scans SAR (TOPSAR). The SNAP

(Sentinels Application Platform) tool was used,

following TOPS Interferometry Tutorial (Veci,

2015). The co-registration of IW1 relevant sub-

swath was performed, the interferogram was

produced, the topographic phase was removed and

the phase filtering was applied. The interferogram

results after the ellipsoid correction are in the figure

below.

Figure 16: Coherence and phase interferogram 2015-2016.

The results were not effective. Although the

interferogram requires a detailed interpretation, this

preliminary analysis did not spot relevant changes

on coherence and phase interferogram. Additionally,

the spatial resolution of the IW acquisitions may not

be sufficient for the monitoring cases.

5 CONCLUSIONS

The study confirmed that several OpenstreetMap

and Google Maps information of the airports are

incomplete or outdated. Although, Sentinels lacks

spatial resolution, they can be an advantage to

validate and trigger mapping improvements. The

acquisition frequency of both Sentinels is considered

a great benefit to assure up-to-date information at a

global scale.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

The recommended SCRAMJET approach for a

target of 200 airports is to do mapping assisted by

Sentinels and eventually other commercial EO data,

and to monitor changes using a Sentinel-2 semi-

automatic change detection method.

Mapping

The mapping solution shall be definitely based in a

combination of multiple sources, including

OpenstreetMap, Sentinels, Google Maps, local

photos and other commercial EO data.

The system shall extract the relevant OSM data

to create an initial mapping information. An

extended EO chain (Figure 17) is recommended with

automatic data acquisition and pre-processing of

Sentinels data. The Sentinels data shall be used for

mapping validation and trigger improvements based

visual inspection of Sentinels and other

complementary sources.

Figure 17: Mapping solution with EO extended chain.

Monitoring Changes

The foreseen solution is also to integrate extended

EO change detection with the OSM change

detection, checking changes every 3 months as

depicted in Figure 18.

The semi-automatic change detection with

Sentinel-2 is suggested, taking advantage of its

update frequency. Implementing an automatic

detections is technically feasible to generate alerts

but it will require a visual inspection to confirm and

trigger the updates. The change detection algorithm

needs to select cloud free images, normalize the

processing and finally fine-tuned algorithm with a

wide number of airports to become fully automated.

The automation shall consider the costs of creating

EO baselines (storage) and processing EO images

(computing).

The changes detected with S2 are real and faster

but they will probably include many false positives

while the changes detected from OSM are more

accurate but more delayed.

Figure 18: Monitor changes extended with S2 detections.

An initial automated proof-of-concept to validate

the study conclusions is recommended as a next

step. A pilot with 3-4 airports shall start by

automating data acquisition and pre-processing for

mapping purposes. The change detection processing

chain with NIR and SWIR bands shall be further

analysed with alternative approaches and automated

afterwards in order to start collecting results and to

fine tune the algorithm.

Figure 19.

ACKNOWLEDGEMENTS

This work was developed by BLUECOVER to

WATERDOG under the ESA BIC Portugal contract.

The work of Gil Gonçalves at INESC-Coimbra was

supported by Foundation for Science and

Technology (FCT) of Portugal under the project

grant UID/MULTI/00308/2013.

A special thanks to WATERDOG and ESA BIC

Portugal for the publishing authorization.

REFERENCES

Aytekin, Ö., Zongur, U., Halici, U., 2013. Texture-based

airport runway detection. IEEE Geosci. Remote Sens.

Lett. 10, 471–475. doi:10.1109/LGRS.2012.2210189

Ban, Y., Webber, L., Gamba, P., Paganini, M., 2017.

EO4Urban: Sentinel-1A SAR and Sentinel-2A MSI

data for global urban services. 2017 Jt. Urban Remote

Sens. Event, JURSE 2017 0–3.

doi:10.1109/JURSE.2017.7924550

Haas, J., Ban, Y., 2017. Sentinel-1A SAR and sentinel-2A

MSI data fusion for urban ecosystem service mapping.

Mapping and Monitoring Airports with Sentinel 1 and 2 Data

Remote Sens. Appl. Soc. Environ. 8, 41–53.

doi:10.1016/j.rsase.2017.07.006

Jensen, J.R., 2015. Introductory Digital Image Processing:

A Remote Sensing Perspective, 4th ed. Prentice Hall

Press, Upper Saddle River, NJ, USA.

Malenovský, Z., Rott, H., Cihlar, J., Schaepman, M.E.,

García-Santos, G., Fernandes, R., Berger, M., 2012.

Sentinels for science: Potential of Sentinel-1, -2, and -

3 missions for scientific observations of ocean,

cryosphere, and land. Remote Sens. Environ. 120, 91–

101. doi:10.1016/j.rse.2011.09.026

Veci, L., 2015. SENTINEL-1 Toolbox SAR Basics

Tutorial. Esa 1–20.

Wang, X., Lv, Q., Wang, B., Zhang, L., 2013. Airport

detection in remote sensing images: A method based

on saliency map. Cogn. Neurodyn. 7, 143–154.

doi:10.1007/s11571-012-9223-z

Zha, Y., Gao, J., Ni, S., 2003. Use of normalized

difference built-up index in automatically mapping

urban areas from TM imagery. Int. J. Remote Sens.

24, 583–594. doi:10.1080/01431160304987

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management