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Charting New Territories: Navigating Deep Learning and Machine Learning in GIS Solutions.
Latest   Machine Learning

Charting New Territories: Navigating Deep Learning and Machine Learning in GIS Solutions.

Last Updated on April 22, 2024 by Editorial Team

Author(s): Stephen Chege-Tierra Insights

Originally published on Towards AI.

Created by the author with DALL E-3

Deep learning and machine learning, what is the difference you may ask? they sound the same for you as a GIS developer. These two terms are being thrown around by data scientists which can be very frustrating at times. However, to properly utilize each tool’s potential for modelling and spatial analysis, a GIS developer must be aware of these differences.

You would be very surprised by the amount people, even seasoned software developers cannot disguise the difference between the two, and you can blame them as these machine learning/ AI terms can be very complicated, especially as they keep on evolving year after year.

Understanding these differences paves the way for more precise and perceptive geospatial solutions by enabling GIS developers to select the best strategy for their unique spatial datasets and analytic objectives.

What are the differences and Similarities?

In the context of GIS, “deep learning” refers to a subset of machine learning methods that automatically identify and extract patterns, characteristics, and correlations from geographical data using multiple-layered neural networks. Without the requirement for explicit feature engineering, these neural networks can process enormous volumes of raw geographical data, such as geospatial sensor data, LiDAR data, or satellite imagery.

According to AWS, to find patterns in data, you can apply both deep learning and machine learning (ML). Both of them use datasets as training grounds for algorithms built on intricate mathematical models. The algorithms look for correlations between inputs and known outcomes during training. Based on unknown inputs, the models can then automatically produce or forecast outputs. In contrast to conventional programming, the learning process is largely automated and requires little human involvement.

The science of teaching a computer program or system to carry out tasks without explicit instructions is known as machine learning or ML. Machine learning algorithms are used by computer systems to process massive amounts of data, find patterns in the data, and forecast precise results for novel or unexpected circumstances.

Neural networks, which are unique mathematical structures fashioned after the human brain, are used in deep learning, a subset of machine learning. Deep learning techniques aim to automate increasingly intricate jobs that normally demand for human comprehension. Deep learning can be used, for instance, to translate documents, describe photographs, or convert audio files into text.

Deep learning also requires much more data than machine learning, which in turn requires significantly more computational power. Machine learning can typically be done with servers running CPUs, while deep learning often requires more robust chips such as GPUs.

Deep Learning and GIS

Deep learning goes deeper into neural networks with multiple layers, enabling it to automatically extract features from raw data. This can be especially useful for complex spatial patterns and high-dimensional data in GIS applications. Machine learning and deep learning both involve training algorithms to learn from data and make predictions.

Deep-learning code for GIS with Google Earth Engine.

 Load satellite imagery from Google Earth Engine
var image = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318");

// Preprocess the image (e.g., resize, normalize)

// Define the architecture of the deep learning model using TensorFlow.js
const model = tf.sequential();
model.add(tf.layers.dense({units: 256, activation: 'relu', inputShape: [img_height, img_width, num_channels]}));
model.add(tf.layers.dense({units: 128, activation: 'relu'}));
model.add(tf.layers.dense({units: num_classes, activation: 'softmax'}));

// Compile the model
model.compile({optimizer: 'adam', loss: 'sparseCategoricalCrossentropy', metrics: ['accuracy']});

// Train the model using Earth Engine data
const trainingData = ...; // Prepare training data
const validationData = ...; // Prepare validation data
const epochs = 10;
const batchSize = 32;
await, validationData, {epochs, batchSize});

// Evaluate the model
const testLoss = model.evaluate(testData)[0].dataSync()[0];
const testAcc = model.evaluate(testData)[1].dataSync()[0];
print('Test Loss:', testLoss);
print('Test Accuracy:', testAcc);

// Make predictions on new data
const predictions = model.predict(newData);
print('Predictions:', predictions);

This example shows how to use Google Earth Engine with TensorFlow.js for deep learning tasks in a basic workflow.

Deep-learning Applications in GIS Include

1. Change detection– Deep learning algorithms can recognize changes in infrastructure, land use, or land cover across time by comparing satellite pictures from the past and present. Monitoring urbanization, deforestation, or other environmental changes can benefit from this.

2. Object Detection-Buildings, roads, cars, and even wildlife may all be identified and recognized in geospatial imagery thanks to deep learning techniques. This is helpful for projects like ecological research, urban planning, and infrastructure monitoring.

3. Image Classification– Deep learning models can categorize satellite imagery or aerial photographs into different land cover classes, such as forests, agriculture, water bodies, urban areas, etc. This computerized classification progression is faster and often more precise than traditional methods.

4. Discover New Patterns– Discovering new patterns can be very exciting as it will reveal things that can not be pointed out by the naked eye, deep learning can help a GIS expert unravel new features about an area of interest.

Compare Deep Learning and Machine learning in GIS

1. Data Necessity-For deep learning models to train efficiently, especially for tasks like semantic segmentation or image classification, significant volumes of data are usually needed. Although machine learning models also gain from large amounts of data, they might be better suited for applications requiring smaller datasets or with fewer processing resources.

2. Model Intricacy– Conventional techniques like decision trees, random forests, support vector machines, or regression models are frequently employed as machine learning models in geographic information systems (GIS). Compared to deep neural networks, which are made up of several layers of connected nodes and are utilized in deep learning, these models are often less complicated.

3. Computing Capabilities-Longer training timeframes and substantial computational resources, such as potent GPUs or TPUs, are frequently needed for training deep learning models. Machine learning methods are more suitable for smaller-scale GIS projects or environments with constrained resources since they usually have faster training times and lower computational requirements.

4. Ideal for Spatial Analysis– Deep learning is particularly good at applications like semantic segmentation in GIS, object recognition, and image classification that require intricate spatial patterns or high-dimensional data. Without considerable feature engineering, machine learning approaches, while still feasible, might not perform as well on some tasks.


In conclusion, with each having advantages and disadvantages of its own, machine learning and deep learning both provide useful tools for spatial analysis in GIS. Interpretable models, effective computing, and adaptability for jobs involving smaller datasets or simpler spatial patterns are all offered by machine learning approaches. However, deep learning is particularly good at large-scale data processing, intricate spatial patterns, and applications where feature engineering could be difficult.

To fully utilize geospatial data for well-informed decision-making and problem-solving, GIS professionals must be able to distinguish between these approaches and select the one that best fits their unique analysis goals, data availability, computational resources, and interpretability requirements.

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