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Object Detection and ML: A Game Changer in the Realm of Spatial Analysis.

Author(s): Stephen Chege-Tierra Insights

Originally published on Towards AI.

Created by the author with DALL E-3

Humans have always had the urge to explore the world we live in, it is a natural born desire dating back to our ancestors, from studying the ocean, space and hidden ancient objects on Earth. The mysteries of the universe excite us as a species.

There is a documentary series that I have been following featuring explorer Ed Stafford called Into the Unknown; where he uses Earth satellites to discover strange phenomena detected by these satellites then heads over to investigate, or what we in the geospatial sector call ground truthing.

These could be objects such as lakes, caves, and Islands that the satellites have detected that seem out of the norm. For example, he ventured into the Amazon forest of Brazil to investigate a blue sea in the middle of the forest that the satellite detected. Check it out if you ever get a chance.

To more accurately and efficiently identify and evaluate anomalies or points of interest in satellite images, object detection is a crucial tool in this discipline. Advances in artificial intelligence and machine learning have led to the development of increasingly complex object detection algorithms, which allow us to efficiently and precisely interpret large volumes of geographical data.

What is Object Detection?

According to IBM, Object detection is a computer vision task that looks for items in digital images. In this sense, it is an example of artificial intelligence that is, teaching computers to see in the same way as people do, namely by identifying and categorizing objects based on semantic categories.

The method of object localization involves defining an object’s bounding box and using it to pinpoint its exact placement inside an image. Another method for figuring out which category a detected object belongs to is object categorization. The goal of the object detection task is to simultaneously estimate the location and kind of object instances in one or more photos by combining the subtasks of object localization and classification.

Object Detection and Spatial Analysis

Object detection in spatial analysis automates the identification of elements, including buildings, roads, vegetation, and bodies of water, improving our capacity to map and comprehend complicated environments. With the utilization of this technology, geographic information systems (GIS) and remote sensing can become much more comprehensive, which will enable the process of tracking changes over time, assessing the effects of human activity, and reaching well-informed judgments for sustainable development. The incorporation of object identification into spatial analysis signifies a noteworthy advancement in our comprehension and engagement with the surrounding environment.

-Automation and Efficiency: Object identification greatly reduces human labor and expedites analysis operations by automating the process of finding and classifying items within spatial data.

-Accuracy and Precision: For accurate mapping and monitoring applications, sophisticated algorithms enhance the accuracy of recognizing certain elements such as roads, buildings, plants, and water bodies.

-Scalability: Large volumes of spatial data may be processed quickly using object detection algorithms, which makes it possible to examine wide geographic regions or track changes over time on a big scale.

-Supports Decision-Making: By providing thorough and up-to-date environmental information, object detection helps informed decision-making processes regarding urban planning, disaster management, environmental conservation, and infrastructure development.

-Integration with Geographic Information Systems (GIS) and Remote Sensing: Their capacities for data presentation, analysis, and modeling are enhanced by the seamless integration of object recognition techniques with GIS and remote sensing technology.

-Enhanced Insight Extraction: Makes it easier to find trends, patterns, and anomalies that could guide research or policy decisions. It also makes it possible to extract important insights from spatial data that may not be immediately obvious.

Types of Algorithms for Object Detection and Spatial Analysis

  1. Traditional Computer Vision Algorithms:

-Haar Cascade Classifiers: These are used for detecting objects in images based on distinct and well-defined features like edges, corners, and textures.

-The Histogram of Oriented Gradients (HOG): This is a feature descriptor that examines and analyses the gradient distribution in images to identify objects. This is somewhat of a popular algorithm for geospatial analysis.

-Feature-based Matching: SURF (Speeded-Up Robust characteristics) and SIFT (Scale-Invariant Feature Transform) algorithms are examples of algorithms that use unique local characteristics which are hard to identify with the naked eye to identify objects.

2. Deep Learning-Based Algorithms:

-Convolutional Neural Network: Because convolutional neural networks (CNNs) can directly learn hierarchical features from data, they are frequently employed for object detection in the case of spatial analysis and machine learning.

-Region-Based CNNs (R-CNN, Fast R-CNN, Faster R-CNN): These methods classify items within those regions and produce region proposals using CNNs.

-Single Shot MultiBox Detector (SSD): A real-time object detection system that predicts and analyses multiple bounding boxes and class probabilities at the same time with a single neural network.

-Only Look Once (YOLO): Another real-time object detection system is called YOLO), which approaches object detection as a regression issue to geographically separated bounding boxes and associated class probabilities.

3. Segmentation-Based Algorithms:

-Mask R-CNN: An addition to Faster R-CNN that includes a branch for segmentation mask prediction in addition to bounding boxes and class labels.

-U-Net: U-Net and its derivatives are used for semantic segmentation jobs where pixel-level accuracy is required. U-Net was first developed for biological picture segmentation.

Example

The subsequent segments offer illustrations of object-oriented techniques utilized for analyzing Landsat 8 surface temperature, with each segment expanding upon the previous one. To create the basis image, run the following snippet: thermal hotspots for a short area of San Francisco

// Make an area of interest geometry centered on San Francisco.
var point = ee.Geometry.Point(-122.1899, 37.5010);
var aoi = point.buffer(10000);

// Import a Landsat 8 image, subset the thermal band, and clip to the
// area of interest.
var kelvin = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318')
.select(['B10'], ['kelvin'])
.clip(aoi);

// Display the thermal band.
Map.centerObject(point, 13);
Map.addLayer(kelvin, {min: 288, max: 305}, 'Kelvin');

// Threshold the thermal band to set hot pixels as value 1, mask all else.
var hotspots = kelvin.gt(303)
.selfMask()
.rename('hotspots');

// Display the thermal hotspots on the Map.
Map.addLayer(hotspots, {palette: 'FF0000'}, 'Hotspots');

Label Objects

// Uniquely label the hotspot image objects.
var objectId = hotspots.connectedComponents({
connectedness: ee.Kernel.plus(1),
maxSize: 128
});

// Display the uniquely ID'ed objects to the Map.
Map.addLayer(objectId.randomVisualizer(), null, 'Objects');

Conclusion

With several advantages like consistency, scalability, precision, and automation, object detection is a potent tool for improving spatial analysis. Using sophisticated algorithms, object detection facilitates accurate and efficient feature recognition within spatial data by utilizing both conventional computer vision techniques and state-of-the-art deep learning methods. This capacity helps with a range of applications, including resource allocation, disaster management, urban planning, and environmental monitoring. In the end, it facilitates well-informed decision-making processes.

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