Advancements in Land Use Mapping through Satellite Imagery

Land Use Land Cover (LULC) mapping is important for planning cities and managing resources. It helps in creating smart and sustainable cities. This article discusses a new method of predicting different land uses in satellite images, focusing on areas with few labeled examples. The goal is to improve how well we can classify things like buildings, roads, trees, and bodies of water in various locations across India.

#The Importance of Satellite Imagery

Satellite images are useful in various fields like defense, resource management, environmental monitoring, and urban planning. These images can cover large areas and have different resolutions. They help us understand and manage the Earth's surface. However, labeling these images manually takes a lot of time and effort. To speed things up, researchers are using advanced computer techniques called deep learning for tasks like object detection and image segmentation. One major application of this is LULC mapping, allowing governments and organizations to keep track of resources and address environmental changes effectively.

#Current Challenges in LULC Mapping

Today, the labels used for different land types in cities mostly come from a few sources, like manually created maps or predictions from existing models. These sources often have limited information and usually focus on just one type of land use, like buildings. For better urban development, it's necessary to have a system that can quickly generate accurate LULC maps, reflecting changes in our landscapes.

Several methods have been tried to predict LULC using supervised and semi-supervised learning. However, these methods often struggle with imbalances in class representation and variations seen in satellite images at different times and locations. Factors like the time of day and seasonal changes influence how buildings and vegetation appear in images, especially given the diverse climate and regions in India. Thus, a method that accommodates these variations is essential.

#Research Question

This study looks to build a more effective framework for segmenting satellite images. The main focus is on creating a model that predicts different land use types accurately, even with sparse labeling.

#Background on Image Segmentation

Image segmentation is a process that helps in classifying different parts of an image. One important model used for this task is called U-Net, which was originally designed for medical images but has shown effectiveness in land use classification. Another advanced model is DeepLab v3+, developed by Google. DeepLab v3+ uses a special technique to capture important features in images, making it particularly good at identifying different land uses.

#Cross Pseudo Supervision (CPS)

Cross Pseudo Supervision (CPS) is a method that helps improve the reliability of predictions. In this method, two segmentation networks are trained differently but work together. One network helps to correct the other, leading to more consistent results.

#Data Sources

#Satellite Images

This study looks at six different high-resolution satellite images from the Indian Space Research Organisation, which capture various areas in India. These images have bands that include near-infrared and visible light.

#Vector Data

For training and testing, vector data is collected for multiple land uses in cities like Bangalore, Mumbai, and Delhi. This data helps in creating the necessary labels for different areas in the satellite images.

#Data Preparation

To prepare the data for analysis, binary masks are created for each land type using vector files. For example, roads are represented using lines, while buildings are marked with polygons. A special technique called the Normalized Difference Vegetation Index (NDVI) is used to determine areas with vegetation.

#Data Chipping

To manage the large satellite images better, they are divided into smaller sections called chips. These chips are created in a way that ensures high-quality data for training the model.

#Model Training

The model training process involves comparing different techniques. Two well-known models, U-Net and DeepLabV3+, are used as baseline models for supervised learning. For the semi-supervised approach, the CPS method is applied.

#UNet and DeepLab v3+

Both UNet and DeepLab v3+ are powerful models commonly used for image segmentation. They are trained using techniques to deal with class imbalance, ensuring that predictions are as accurate as possible.

#CPS Methodology

In the CPS approach, two models help each other improve their predictions. This collaborative effort enables the system to learn effectively from both labeled and unlabeled data. During training, losses from different predictions are combined to enhance overall performance.

#Model Evaluation

To assess the model's performance, different techniques are used to ensure proper evaluation. Since there are challenges with sparse labeled data, metrics like recall are used instead of traditional accuracy measures. Recall focuses on correctly identifying the presence of a feature while minimizing false negatives.

#Results

The findings show that the CPS method significantly improves prediction accuracy compared to models that do not use this approach. The average recall score from various experiments indicates that this semi-supervised method outperforms traditional supervised methods.

#Future Improvements

While the current model demonstrates good performance, there are still opportunities for enhancement. Implementing strategies that adjust the importance of different classes in the loss function could further improve results. Additionally, applying techniques to remove cloud cover and correct atmospheric distortions in satellite images will lead to better quality inputs for the model.

#Conclusion

In summary, the study explores the use of sophisticated deep learning techniques for effective Land Use Land Cover segmentation. By leveraging sparse labeled data and implementing the CPS method, the research demonstrates significant improvements in classifying different types of land use. The findings highlight the importance of being able to adapt to variations in satellite images, ultimately supporting smarter urban planning and resource management. The ongoing work aims to refine these techniques further, ensuring that they remain effective as landscapes continue to change.