IEEE Geoscience and Remote Sensing Magazine - December 2022 - 103

example, training on crop fields in moderate climate regions
cannot be applied in tropical regions. Similarly, data
used to train AI/ML models in, say, identifying building
types are not reflective and representative of what is seen in
diverse real-world contexts. While data scarcity is a reality,
it is the authors' responsibility to clearly and honestly state
the extents and limitations of their datasets, level of diversity,
and overall approach as well as resulting limitations in
the scope and applicability of the research models/findings.
It is important here to draw a distinction while acknowledging
the relationship between datasets and models. It may
be difficult to identify limitations of models (e.g., of feature
extractor networks trained in an unsupervised manner),
but the scope of the dataset used to train a model is always
known. The limitations of the dataset are also likely to impact
the output provided by the model trained using that dataset.
Therefore, even in models trained in an unsupervised
manner, the underlying dataset and its limitations must be
identified and disclosed. In the case of AI in health care, for
example, it has been discovered that models trained primarily
on datasets consisting of male heart patients are unable
to accurately predict heart disease in women [106]. Here, the
limitations of the dataset (and by implication, of the model)
can be known and predicted if the scientists and developers
involved clearly state that most of the training data are from
male heart patients so that doctors are more careful when
relying on this dataset when faced with a female patient.
Similarly, closer to home, i.e., in EO research, if a model for
predicting slum areas is trained on data from Brazil, it is necessary
to identify the scope and origin of the dataset so that
it is not (fully) relied on to predict slum areas in Nigeria and
other regions of the world where the population density, local
cultures, and average income levels may be very different.
To increase the diversity of available data, methods to incentivize
and maximize contributions of more diverse data
(in a manner that secures the privacy of contributors) from
HDI Rank
1,000
020406080
LUX
100
120
140
160
Number of Satellites | Nonlinear
Exponential (Number of Satellites | Nonlinear)
10,000
RUS
USA
100,000
GBR
NOR
OOR
SUI
1 Million
SWE
GER
AUS
SLO
NED
KOR
ESP
AUT
GRE
POL
10 Million
CHI
TUR
POR
IRI
ROU
SRT
COL
GHA NEP
KEN
100 Million
BAN
ETH
ANG
N
PAK
NGR
ITA
CZE
LATHUNKSA
SVK
KAZ
QAT
ARG
BLR
MAS
CRC
UKR
MEX
THA
BRA
PER
ALG
ECU
JOR
BOL
PHIINA
PAR
P
VEN
V
RSA
EGY
SAGEGY
VIE
GUA
MA
MAR
IND
RWA
TKM
LAO
AZE
MGL
PNG
CHN
BUL
MRI
BHU
FIN BEL
NZL
SGP
DENDE
CAN
ISR
JPN
LTU
FR EST
UAE
URU
180
FIGURE 4. The correlation between the relative number of satellites in relation to the number of inhabitants of a country and the country's
ranking in the Human Development Index (HDI). A correlation between the development of the country and its number of owned satellites
can be seen. The chart is based on the analysis of the satellite catalogue in [91], the number of inhabitants for each country in [92], and the
ranking of the countries HDI in [93]. SUI: Switzerland; BEL: Belgium: LTU: Lithuania; ESP: Spain; AUT: Austria; KSA: Saudi Arabia; SVK: Slovakia;
MAS: Malaysia; CRC: Costa Rica; ROU: Romania: AZE: Azerbaijan; THA: Thailand; PER: Peru; ECU: Ecuador; ALG: Algeria; IRL: Ireland;
MGL: Mongolia; PAR: Paraguay; TKM: Turkmenistan; INA: Indonesia; RSA: South Africa; VIE: Vietnam; MAR: Morocco; BHU: Bhutan; USA:
United States; GBR: Great Britain; RUS: Russia; NOR: Norway; FIN: Finland; ISR: Israel; NZL: New Zealand; SGP: Singapore; DEN: Denmark;
JPN: Japan; CAN: Canada; FR: France; EST: Estonia; UAE: United Arab Emirates; SWE: Sweden; AUS: Australia; GER: Germany; NED: The
Netherlands; SLO: Slovakia; KOR: South Korea; ITA: Italy; CZE: Czech Republic; LAT: Latvia; HUN: Hungary; GRE: Greece; POL: Poland; CHI:
Chile; POR: Portugal; TUR: Turkey; ARG: Argentina; QAT: Qatar; KAZ: Kazakhstan; BUL: Bulgaria; BLR: Belarus; CHN: China; UKR: Ukraine;
BRA: Brazil; MEX: Mexico; COL: Columbia; JOR: Jordan; VEN: Venezuela; BOL: Bolivia; PHI: Philippines; IND: India; GUA: Guatemala; LAO:
Laos; PNG: Papua New Guinea; RWA: Rwanda; GHA: Ghana; NEP: Nepal; PAK: Pakistan; ANG: Angola; KEN: Kenya; NGR: Nigeria; BAN:
Bangladesh; ETH: Ethiopia; and URU: Uruguay.
DECEMBER 2022 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
103
People per Satellite
IEEE Geoscience and Remote Sensing Magazine - December 2022

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