How to Perform Quality Check on Agriculture Satellite Imagery Dataset

Satellite images from a vast distance can capture a wide-angle viewing zone, making artificial intelligence more effective in a range of fields. Furthermore, AI systems that employ satellite images may learn to foresee different circumstances after analyzing the situation with satellite imaging datasets created particularly for AI models.

Furthermore, a considerable quantity of training data is required to build such models using machine learning or deep learning approaches. Different sorts of aerial views of various objects, such as agricultural fields, forests, cities for urban planning and management, and numerous other manmade structures, are included in this satellite image data.

Significance of high-quality Satellite Image Dataset

It doesn’t matter how brilliant a model architecture is if it isn’t trained with adequate data. On the other hand, if given a suitable dataset for training, even the most basic model architecture may be sufficient to complete the task.

And, while there has been a lot published on machine learning model building, we believe that dataset preparation has been overlooked, which is fair given that it isn’t a particularly exciting subject and takes a long time to do right. Unfortunately, grasping the need to ‘build a decent dataset’ was a long and unpleasant process.

So, what constitutes a “good” dataset? Below are the few features of good training datasets for satellite imagery.

Data dispersion data should span the whole input spectrum, if not all of it.

Every class should have adequate representation in the dataset to ensure data coverage.

• Data precision data should be extremely relevant to the job at hand and as near to that utilized for inference as feasible in terms of quality, format, and other factors.

• Feature engineered– Data should enable the machine learning model to learn what we want it to learn (appropriate features)

• Data transformation data obtained nearly always cannot be utilized as-is, and a suitable data transformation pipeline can help to simplify the model design.

• Data volume whether the ML model is constructed from the ground up or learning is transferred from another model, data availability is crucial.

• Data split data is often split into three chunks: training (75%), validation (15%), and test (10%), and it’s critical to verify that no ‘duplicate/same’ data exists throughout these chunks and that the samples are dispersed appropriately.

Also Read :How to Improve Computer Vision in AI for Precision Agriculture

Advice on how to make satellite images datasets for agricultural purposes

1. Know the truth by using ground truth data.

The Ground Truth (GT) data produced is used at almost every level of the project. In the case of agricultural satellite imagery, it should have all of the characteristics about the crop fields, which will aid in identifying them separately, so that the information can be given to ML models via proper datasets for accurate feature extractions.

In short, the purpose of the Ground Truth data should be to produce a full, well-balanced, and evenly dispersed dataset of satellite pictures. It should also act as a guide for recognizing various things of interest in order to assist accurate and thorough labelling.

2. Know the landscape in terms of spatial distribution.

To understand the terrain of an area of interest, look at satellite images from various time periods/seasons, include images from various terrains in the dataset, consider the challenges with images from various areas while labelling/marking, and address those challenges as much as possible with appropriate labelling.

3. Know the growth cycle in terms of temporal distribution.

At this stage of dataset construction, talking to subject matter experts/farmers in the area of interest to understand the temporal data to be recorded is crucial.

4. Image quality be aware of what you’re looking at.

Though there are free satellite imaging databases accessible to the public, high-quality photos for uses such as agricultural growth detection, crop type identification, and so on are costly. The better the quality, the more expensive it is.

5. Ensure that not only the major classes/objects of interest but also the associated classes/objects are labelled and masked precisely.

Also Read :Role of Image Annotation in Applying Machine Learning for Precision Agriculture

Conclusion

Most importantly, we must recognize that dataset creation is an iterative process in which feedback and inputs from various stages such as ground truth data analysis, data analysis and engineering, labelling, and model building should all be taken into account in order to improve the dataset in an iterative manner.

Anolytics can provide high-quality satellite image data sets for machine learning AI development. It produces training data sets for computer vision-based AI models using a world-class data annotation service and expertise in image annotation. It may employ a variety of image annotation techniques to annotate satellite photographs in order to provide datasets for machine learning models that are as accurate as possible while being cost-effective.