Representing Geographic Space; Discrete and Continuous

Geographic data can be broadly classified into two categories - discrete and continuous. Discrete geographic data is information that is specific to particular locations, and can be represented by different types of values like nominal, ordinal, interval, and ratio. This type of data includes features like landownership, soils classification, zoning, and land use.

When represented on a map, discrete data typically has well-defined boundaries and is typically represented in polygon format. For example, the shape of a property boundary or a zoning district can be considered as discrete geographic data. In addition, point and line datasets such as tree locations, rivers, and streets are also considered as discrete data.

On the other hand, continuous data refers to information that does not have a clearly defined boundary and can be measured at every point on a map. Continuous data is often represented as surfaces and includes datasets like elevation, rainfall, pollution concentration, and water tables. When represented on a map, continuous data can be visualized as a continuous gradient or a smooth transition of values from one point to another.

In summary, discrete geographic data pertains to information that is specific to certain locations and has well-defined boundaries, whereas continuous geographic data pertains to information that is present throughout an area and has no clear boundaries. Understanding the difference between these two types of geographic data is essential for effective analysis and visualization of spatial data.

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Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification: - You have prior knowledge about the c...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

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History of GIS

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Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

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