The work was presented at European Conference of Machine Learning (ECML-PKDD), 2018 and focused on modelling and predicting urban growth using remote sensing and socioeconomic data.

1. Computational Model for Urban Growth Using Socioeconomic
Latent Parameters Piyush Yadav (Lero- Ireland )
Shamsuddin Ladha(Tata Research -India)
Shailesh Deshpande(Tata Research- India)
Edward Curry(Lero- Ireland)

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• Urban Growth
• Land Use Land Cover Change
• Urban Growth Models and Limitation
• Proposed Model
• Study Area
• Datasets and Pre-processing
• Results
• Conclusion
Outline

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World Population is growing
Increased Economic Activities
Increased Urban Growth Rate
Urban Growth
An Aerial View of a part of study area in 2006 and 2014

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A KEY ASPECT OF
URBAN GROWTH
IS AFFECT ON
LAND USE LAND
COVER CHANGE
LAND COVER
INDICATES
THE PHYSICAL
LAND TYPE SUCH
AS FOREST OR
OPEN WATER
LAND USE
DOCUMENTS HOW
PEOPLE ARE USING
THE LAND SUCH AS
AGRICULTURE
Land Use Land Cover Change(LULCC)

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Factors Affecting Land Use Land Cover
• Predominantly, change over space but
remain relatively static with respect to
time.
• Digital Elevation Model (DEM)
Spatial
Factors
• Change over both time and space.
• Proximity to the primary roads
Spatio-
temporal
Factors
• Change over time but spatially static
for a given study area.
• National Gross Domestic Product
(GDP)
Temporal
Factors
Direct
Factors
Indirect
Factors
Land Use
Land Cover
Change

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Urban Growth Models
Thus the lattice based spatio-temporal models, e.g. Cellular Automata
(CA) and Logistic Regression (LR), are effectively used to model the
spatial geographic processes.
Whereas, the MC model predicts the quantum of growth along with the
rate of transfer between different LULC classes (without specifying the
exact growth locations).
LULC images of two distinct time instances are taken and the
probabilities are computed using the frequency of change from one
LULC class to another and generate transition probability matrix.
Urban Growth models are used for prediction of land use land cover
(LULC) changes. LULC modeling is extremely difficult due to complex
interactions between multi-scale factors.
Schematic of an integrated Markov
Chain model

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• Markov model is built only on the cross tabulation of transition frequencies from
the existing land cover images, future transition probabilities have to be estimated
from the existing ones.
• Some elements of the transition probability matrix for the future time period
are derived based on a simple power law.
• 𝒂 𝟏𝟏 is the transition probability of LC Class 1 persisting after time period 𝒕, then
for time period 𝟐𝒕 the persistence rate remains unchanged and hence the new
probability value is 𝒂 𝟏𝟏
𝟐
.
• In world wide process of increasingly rapid urbanization the assumption of
persistent rate change is unrealistic and strongly constrained.
Persistent Rate of Change
• It is well known that processes such as LULCC and socio-economic changes are
linked and interactive (et al. Lefebvre 1991)
• MC model does not allow incorporating the effect of temporal variables such as
macro-economic factors, socio-economic factors, etc.
Temporal Factors
Limitation to Present Models

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Our Contribution
Hidden Markov
Model
Introduction of Hidden
Markov Model (HMM)
Temporal Factors
Incorporate temporal
factors in LULC change
modelling using HMM.
Model the underlying
temporal factors as
Gaussian distributions,
conditioned on the
hidden states, to learn
land cover type
transition probabilities
Integrate
Integrate our model
with other spatio-
temporal models such
as Logistic Regression
(LR) to yield richer
integrated models than
the corresponding MC
based integrated
models.
An urban growth model with
multi-scale direct and indirect
factors impacting LULC changes

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Our Model
A Hidden Markov Model with hidden states
(V, I, S) and sample emissions (GDP and
Liquidity)
Proposed urban growth model: HMM
integrated with Logistic Regression model

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Study Area: Pune
• Tier-A city situated in the state of Maharashtra, India.
• Located 560 m above the sea level.
• Famous for Information Technology and Automobile industries and various research institutes.
• Considered 45 sq. km of the city area which have gone under rapid urbanisation.

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Temporal Growth Factors
Gross Domestic Product
National. Amount of goods and services produced within the border of a
country in a specific time interval.
Interest Rate Cycle National. Revised bimonthly. A tight monetary policy affects the overall
investment policy which leads to slowdown and vice versa.
Consumer Price Index National. Low inflation creates developmental investment environment.
Gross Fixed Capital Formation
National. Amount that government spends in the capital formation(such
as infrastructure building, land improvements) of the country. Greater
the GFCF investment higher is the rate of urbanization .
Urban Population Growth Rate
National. In order to accommodate a higher influx of people, cities
are expanding along their outskirts, leading to the growth in urban
agglomerate.
Electricity Consumption Regional. Typically, regions with higher electricity demand grow
faster than those with lesser demand.
Road Length Added
Regional. Better connectivity of a region helps in better transportation
and thus provides impetus to growth by allowing setup of new industrial
complexes and other infrastructure services.

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Temporal Growth Factors Data
GDP growth rate (%)
Absolute average CPI Inflation (%)
Gross fixed capital formation (%GDP)
Urban population growth rate (%)
Bimonthly interest (repo) rate (%)
Per capita electricity consumption in
kilowatt-hours

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Land Use Land Cover (LULC) Data
LULC data is required for HMM hidden states and LR models as an input.
Time
period
Yearly, 2001 to 2014 (between March to
April)
Latitude 18.38847838°N - 18.79279909°N
Longitude 73.64552005°E - 74.07494971°E
Bands 1 to 7
Resolution 30m
Pixels 1500 𝑥 1500
Landsat 7
Landsat-7 Specifications

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LULC Data Pre-processing
Scan Line Correction (SLC)
Atmospheric Correction
Solar Correction
• In 2003 Landsat-7 SLC in ETM+ instrument has developed a fault thus creating
some black lines in the captured images.
• Image Smoothening using windowing.
• Electromagnetic radiation captured by the satellite sensors is affected because
of the atmospheric interference such as scattering, dispersion, etc.
• Subtract the digital number (DN) of water pixels in band 4 (infrared band) as it
has very low water leaving radiance.
• DN values were then converted to spectral radiance.
𝑳 = 𝑳 𝒎𝒊𝒏 +
𝑳 𝒎𝒂𝒙
𝟐𝟓𝟒
−
𝑳 𝒎𝒊𝒏
𝟐𝟓𝟓
𝒙 𝑫𝑵
• For clear Landsat images, solar correction of the images was done by converting
spectral radiance to exoatmospheric reflectance.
𝝆 𝒑 =
𝝅 ⋅ 𝑳 𝝀⋅ 𝒅 𝟐
𝑬𝑺𝑼𝑵 𝝀 ⋅ 𝒄𝒐𝒔𝜽 𝒔

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• Classified into seven broad LULC classes on the basis of the nature of the
landscape.
• Forest Canopy, Agriculture Area, Residential Area, Industrial Area, Common
Open Area, Burnt Grass, Bright Soil, and Water Body.
Classes
• For classification a labeled set of pixels for each class of interest was collected
(500 to 3000 samples per class). The feature vector for each pixel consisted of
all seven band values.
• Support Vector Machines
• Manual Correction (Concrete and Quarry)
SVM Classification
• Vegetation, Impervious Surface, and Soil
VIS Classes
LULC Data Classification

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A Quick Recap
LULC Data

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Spatio-Temporal Factors
Digital Elevation Model (DEM) and Slope
Proximity to primary roads:
Mask
CARTOSAT 1
Water bodies were masked out from the LULC image
3 D View
DEM Image
Primary Road Layers

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Results
HMM Experiments
Computed MC transition probabilities for 2001-2002, Learned HMM transition probabilities for
2014, Computed MC transition probabilities for 2014
• Used Gaussian HMM library in Scikit Learn
• We designed a HMM with the three hidden states (V, I, and S) and temporal factors
• HMM was initialized with MC transition probabilities for the year 2001 to 2002
• A stable model was obtained empirically after 50000 iterations with a threshold of less than 0.01

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Results
Land Change Modelling Experiments
• Terrset’s Land Change Modeler.
• Transition sub-models were defined for four LC change types, i.e., V to S, V to I, S to V, and S to I.
• Slope gradient and primary roads layer were used as the primary driver variables .
𝒔𝒖𝒊𝒕𝒂𝒃𝒊𝒍𝒊𝒕𝒚 =
𝟏
𝒔𝒍𝒐𝒑𝒆 𝒈𝒓𝒂𝒅𝒊𝒆𝒏𝒕 𝟎.𝟏
• Suitability map. Greater the value higher the suitability and vice-versa.
• Suitability for urbanization is high in areas such as roads, low lying
river basin, and around the urbanized areas where the slope gradient
is less.
• Towards, the south end the suitability drops significantly, as the area
has hills and valleys.
• Four of the sub models were built using Logistic Regression.

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Results
Soil to Impervious Soil to Vegetation
Vegetation to Impervious Vegetation to Soil
Heat maps depicting transition probabilities from one state to another

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• The two models were then used to predict changes for the year 2014.
Results
Actual land cover image of
2014 obtained from
classification
Predicted land cover image
of 2014 (HMM-LR)
Predicted land cover image
of 2014 (MC-LR)
• Visually it is evident that the HMM based predicted image is significantly better, in terms of similarity with
the actual classified LC image than the MC based predicted image .

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HMM-LR MC-LR
V I S V I S
Precision 0.48 0.49 0.60 0.54 0.38 0.34
Recall 0.48 0.52 0.59 0.54 0.32 0.39
Results
• Blob Analysis of urban and non urban regions. Blobs denote concentrated urban regions.
• Green blobs are true positives, blue blobs are false negatives, and red blobs are the false positives.
• HMM-LR false positives are smaller in size and less dense than those of the MC-LR. The HMM output is well
balanced and resembles the actual output better.
• 11% increment in precision of the persistence of Impervious Surface (I) is observed.
• Precision of Soil (S) class type has jumped up by 26%.
• Drop in the precision of Vegetation (V) class type by a marginal 6% . This is because vegetation cover is an
outcome of relatively easy process as compared to S and I .
Blob Analysis of urban areas. Left to right: (i) Actual, (ii)
MC-LR, (iii) HMM-LR
Precision and Recall for integrated models

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Conclusion
• Markov Chain (MC) models are limited in their urban prediction capabilities due to the
assumption of constant rate of persistence of land cover class types and inability to model the
temporal factors.
• We have proposed a new temporal model using Hidden Markov Model.
• We have demonstrated the usefulness of our model over MC by predicting urban growth for
an upcoming city of India (Pune).
• We believe that this inquiry into HMM based models provides yet another tool that will
equip the urban modelers, planners and decision makers to better design sustainable
urban environments.
• 11% and 26% increment of precision in Impervious Surface and Soil Class respectively.

24. THANK YOU
QUESTIONS

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