KEY CONCEPTS & TECHNIQUES IN GIS Part 8 ppt

on Spatial Information Theory (COSIT) series is to a large degree devoted to the

development of methods of qualitative spatial reasoning; unfortunately not much of

the work presented there (1993–2005) has made it into readily available software.

11.2 Neural networks

With the advent of large spatial databases, sometimes consisting of terabytes of data,

traditional methods of statistics such as those described in the previous chapter

become untenable. The first group of GIScientists to encounter that problem was

remote sensing specialists, and so it is no surprise that they were the first to ‘dis￾cover’ neural networks as a possible solution. Neural networks grew out of research

in artificial intelligence, where one line of research attempts to reproduce intelli￾gence by building systems with an architecture that is similar to the human brain

(Hebb 1949). Using a very large number of extremely simple processing units (each

performing a weighted sum of its inputs, and then firing a binary signal if the total

input exceeds a certain level) the brain manages to perform extremely complex tasks

(see Figure 64).

GEOCOMPUTATION 79

Feature vector

Weights

(parameters)

Non–linear

Non–decreasing

Activation function

Threshold effect described as

an additional constant input:

X0 = −1 (threshold)

X0 = +1 (bias)

X = (X1,X2,...Xn)

t X0 = 1

X1

X2

Xn

W1

W2

W3

W0

v y

n

i = 0

Wi

Xi ϕ(v)

Figure 64 Schematics of a single neuron, the building block of an artificial neural

network

Using the software (sometimes, though rarely, hardware) equivalent of the kind of

neural network that makes up the brain, artificial neural networks accomplish tasks

that were previously thought impossible for a computer. Examples include adaptive

learning, self-organization, error tolerance, real-time operation and parallel process￾ing. As data is given to a neural network, it (re-)organizes its structure to reflect the

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