Kyoto University
October 4, 2023

RESPONSIBLE AI FOR
EARTH OBSERVATIONCAROLINE GEVAERTc.m.gevaert@utwente.nl*This work is partially funded by the NWO-VENI project no. 18091

AI IN EARTH OBSERVATION
12%

Percentage of GIS/EO publications on deeplearning

Number of GIS/EO publications on deep learning

3500
3000
2500
2000
1500

1000
500

10%
8%
6%
4%2%0%02013

2014

2015

2016

2017

2018

Year

2019

2020

2021

2022

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Year

Academic publications in Scopus
TITLE-ABS-KEY ( "deep learning" AND ( "GIS" OR "Earth Observation" OR "Remote Sensing" OR "geospatial" ) )Source: Scopus

GUIDELINES & FRAMEWORKS

Safety &Security

Privacy

Accountability

Fairness & Nondiscrimination

Transparency &Explainability

Professional
Responsibility

HumanControl

Source: Fjeld et al.(2020) Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-BasedApproaches to Principles for AI. Berkman Klein Center Research Publication No 2020-1

FAIRNESS / BIAS

TYPES OF BIAS

Suresh, H. & Guttag, J. V. A Framework for Understanding Unintended Consequencesof Machine Learning. (2020).

Source: Gevaert, C. (2022) EARSeL Cyprus 2022.

Attribute 1FAIRNESS?
11TPFP

1TPFN

2FPTN

Attribute 2TP

121TPFN2FPTNTN
3SIMPLETPFN
223221121TP

MULTI-CLASS

AUDIT FOR BIASESBIAS METRICS

Source: Aequitas documentation
https://dssg.github.io/aequitas/metrics.html

EXAMPLE – BUILDINGDETECTION IN
TANZANIA•

100 study areas of 250 x 250 m•

Distributed over poverty groups, city size•

Manually digitized over Google Satellite imagery→ could cause issues regarding temporal
differences or shift in imagerySource: Gevaert, C. (2022) EARSeL Cyprus 2022.

SENSITIVE ATTRIBUTES
CITY SIZE

WSF-2019
https://download.geoservice.dlr.de/WSF2019/files/POVERTYWorldPop
DOI : 10.5258/SOTON/WP00290

BUILDING SIZEACCURACY – EXAMPLE LOW POVERTY, BIG CITY
OSM

Source: Gevaert, C. (2022) EARSeL Cyprus 2022.

BING

GOOGLEACCURACY – EXAMPLE HIGH POVERTY, RURAL
OSM

Source: Gevaert, C. (2022) EARSeL Cyprus 2022.

BING

GOOGLE

PRECISION𝑻𝑷𝒈
෡ = 𝟏| 𝒀 = 𝟏, 𝑨 = 𝒂𝒊 )𝑷𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏𝒈 == 𝑷𝒓(𝒀𝑳𝑷𝒈Fraction of true positives of a group divided by thelabelled positives (=true positive + false positive) of thegroup.

OSM

Source: Gevaert, C. (2022) EARSeL Cyprus 2022.

OSM
More precise for larger cities, biased against smaller cities
More precise for lower poverty levels, biased for poverty
GoogleMore precise for rural areas, biased against smaller cities
More precise for higher poverty levels, biased for poverty

GOOGLEEXPLAINABILITYMOTIVATIONS FOR EXPLAINABLE AI

ControlImprove

Gevaert (2022) Explainable AI for earth observation: A review including societal and regulatory perspectives,
Int. Journal of Applied EO and Geoinfo, 112, 102869. doi: 10.1016/j.jag.2022.102869

DiscoverJustify

TYPES OF EXPLAINABLE AI IN EARTH OBSERVATION

Gevaert (2022) Explainable AI for earth observation: A review including societal and regulatory perspectives,
Int. Journal of Applied EO and Geoinfo, 112, 102869. doi: 10.1016/j.jag.2022.102869

INTERPRETABLE MODELS

Marcos, D., Lobry, S., Tuia, D. 2019. Semantically Interpretable Activation Maps: what-where-how explanations with CNNs. ICCV 2019

INTERPRETABLE MODELS

Marcos, D., Lobry, S., Tuia, D. 2019. Semantically Interpretable Activation Maps: what-where-how explanations with CNNs. ICCV 2019

FEATURE SELECTION &IMPORTANCEFeatures:
Color, texture, time, other sensors, elevation….

••

49% of the publications
Identify influential factors & induce sparsity.

SALIENCY MAPS

Source: Najmi, A. (2021) Integrating Remote Sensing and Street View Images to Map Slums Using Deep Learning Approach (MSc thesis).

CRITIQUESARE RANDOM FORESTS INTERPRETABLE?

Gevaert (2022) Explainable AI for earth observation: A review including societal and regulatory perspectives,
Int. Journal of Applied EO and Geoinfo, 112, 102869. doi: 10.1016/j.jag.2022.102869

EXPLAINABILITY – IS THISMODEL SUITABLE?
GENERALIZATION CAPABILITYACCRADAR ES SALAAMZANZIBAR

Gevaert, C.M. & Belgiu, M. (2022) Assessing the generalization capability of deep learning networks for aerial
image classification using landscape metrics. International Journal of Applied Earth Observation and Geoinformation, 114.

Gevaert, C.M. & Belgiu, M. (2022) Assessing the generalization capability of deep learning networks for aerial
image classification using landscape metrics. International Journal of Applied Earth Observation and Geoinformation, 114.

SIMILARITY SCORE

Gevaert, C.M. & Belgiu, M. (2022) Assessing the generalization capability of deep learning networks for aerial
image classification using landscape metrics. International Journal of Applied Earth Observation and Geoinformation, 114.

LANDSCAPE METRICS

CORRELATION

Gevaert, C.M. & Belgiu, M. (2022) Assessing the generalization capability of deep learning networks for aerial
image classification using landscape metrics. International Journal of Applied Earth Observation and Geoinformation, 114.

Reference data

K-means clustering (k=2)

Gevaert, C.M. & Belgiu, M. (2022) Assessing the generalization capability of deep learning networks for aerial
image classification using landscape metrics. International Journal of Applied Earth Observation and Geoinformation, 114.

CONCLUSIONS
Biases are also present in EO data – methods for auditing are there,
but the challenge is identifying the sensitive attributes and raisingawareness that we need to audit for them.
Need methods to predict generalizability. – needed to provide
explanations that legislation requires & understand when a model canbe used.
Explanations in ML for EO not new, but changing
Limitations of explainability in EO:▪ Which algorithms are considered interpretable▪ Focus on technical audience▪ Lack of testing of whether explanations are adequate

Kyoto University
October 4, 2023

RESPONSIBLE AI FOR
EARTH OBSERVATIONCAROLINE GEVAERT
c.m.gevaert@utwente.nl*This work is partially funded by the NWO-VENI project no. 18091