Pretrained vs Fine-Tuned Models: Best for GeoAI?
- May 29, 2025
- 4 min read
Updated: 5 days ago
The process of transforming the geospatial sector through the application of Artificial Intelligence (AI) involves the utilization of technologies that allow machines to interpret images from satellites, drones, LiDAR point clouds, and aerial photographs of the Earth. Nevertheless, developing deep learning algorithms from scratch calls for huge amounts of labeled data, powerful GPUs, and time for development. Therefore, the necessity of using pre-trained models for refinement has appeared in modern workflows of GeoAI.
If you are creating a land cover classification model, identifying buildings on satellite images, establishing transportation routes, conducting vegetation health analysis, or analyzing LiDAR point clouds, you should be aware of the difference between the pre-trained models and the fine-tuned ones.
What Is a Pretrained Model?
A pretrained model refers to a deep learning model that has been trained on a large amount of data prior to executing a new task. Instead of starting with random weights, the model begins with the experience it has gathered when trained on millions of images.
Some of the well-known pretrained datasets include:
ImageNet
COCO
BigEarthNet
EuroSAT
SpaceNet
xView
LoveDA
As these models have already been trained to understand general visual patterns, such as edges, shapes, colors, textures, objects, and spatial relations. Developers can save time by using this previously acquired knowledge rather than training the model from the very beginning.
What Is Fine-Tuning?
The technique of fine-tuning refers to the practice of using an already-trained model and subjecting it to further training on a small set of data related to the subject matter at hand.
Instead of having to start from the ground up, in this case, we are altering the knowledge of the offline-trained model.
For example:
Detection of buildings from your satellite images
Classification of a crop using multispectral images
Extracting roads
Mapping floods
Detection of changes in forests
Detection of solar panels
Detection of vehicles
Inspection of utilities
Fine-tuning has proven its efficacy over the initial training on the specific task with a pretrained model.
Understanding Transfer Learning
Transfer learning is a process in which existing models are used and adapted for a new purpose.
Rather than going through the process of training a model from the very beginning, the features of the old model can be modified for application.
The steps to this process are as follows:
Train the algorithm on a large dataset.
Save the parameters and weights.
Load the previously learned parameters and weights.
Change the final stage of the model if needed.
Train on a smaller set.
Test the model.
Transfer learning reduces the amount of time needed for model construction and increases the accuracy of predictions made.
Pretrained Models vs. Fine-Tuned Models
Feature | Pretrained Model | Fine-Tuned Model |
Training Time | Very Low | Moderate |
Accuracy | Good | Excellent |
GPU Requirements | Low | Medium to High |
Labelled Data Needed | Minimal | Moderate |
Domain Knowledge | Generic | Domain-specific |
Development Cost | Low | Medium |
Suitable for Production | Sometimes | Yes |
Best For | Rapid prototyping | Real-world deployment |
Why Pretrained Models Are Popular
The reasons for the popularity of pretrained models:
Pretrained models come with multiple benefits.
Faster Development
Developers save weeks of costly training.
Lower computing cost
Training deep neural networks from scratch may take days with several GPUs involved.
Using pretrained weights cuts down computing needs drastically.
Better performance
Even small datasets can yield excellent results with transfer learning.
Reduced data requirements
Most GIS organisations only have a few thousand labelled images.
Fine-tuning is effective even with small datasets.
Advantages of Fine-Tuning
With fine-tuning, the pretrained model is tailored to a specific purpose.
The advantages are:
High predictive accuracy
Improved feature extraction
Improved generalisation
Reduced false alarms
Better results with aerial imagery
More reliable detection
Higher accuracy in segmentation
When Should You Use a Pretrained Model?
When might a pretrained model be a good idea?
When you are putting together a proof of concept
When working with insufficient data
When you lack enough GPU resources
When deployment needs are pressing
When you are testing an idea
When Should You Fine-Tune a Model?
Fine-tuning is usually the preferred option when
Accuracy is one of the goals.
The model is to be used in production.
The project involves specialist images.
The project deals with multispectral or hyperspectral images.
The job needs satellite imagery.
The project involves working with LiDAR data.
The project requires detection of infrastructure.
Popular Use of Pre-trained Models
Some of the most popular pre-trained models are listed below:
ResNet
EfficientNet
DenseNet
MobileNet
ConvNeXt
Vision Transformer (ViT)
Swin Transformer
SegFormer
DeepLab V3+
U-Net
Mask R-CNN
Faster R-CNN
YOLOv8
Segment Anything Model (SAM)
PyTorch Fine-Tuning Example
With the use of PyTorch, you can accomplish this:
Load the pre-trained model (ResNet)
Replace the final classification layer.
Freeze some of the layers in the beginning.
Train your data
Keep track of the loss and measures of effectiveness.
Save the newly trained model.
This technique is widely used in satellite image classification, aerial object detection, and land cover mapping.
Common Mistakes
To avoid common mistakes while performing fine-tuning:
Fine-tuning with a lack of labeled data
Picking an extremely high learning rate
Training all of the layers without need
Failing to apply data augmentation
Fine-tuning on an imbalanced dataset
Not going through validation.
Overfitting due to too many epochs
Pretrained models and their variants have revolutionized what can be achieved with GeoAI, allowing for the development of advanced deep learning systems without the need to invest a great deal of money in the training process. In particular, pretrained models have improved speed and efficiency while providing a strong foundation for further corrections, while fine-tuning can adapt the models to specific geospatial applications such as satellite image classification and interpretation, drone mapping, LiDAR analysis, semantic segmentation, and object detection.
Using pretrained models and fine-tuning when necessary constitutes the most appropriate strategy for GIS, remote sensing, and environmental monitoring projects, as such an approach offers a better balance between cost, efficiency, and quality of results. With the advent of new foundation models and efficient techniques of fine-tuning, companies will be able to benefit even more from geospatial AI solutions.
By understanding how to use pretrained models and how transfer learning works, professionals in the field of GIS and AI will be able to develop smarter and faster GeoAI solutions.
For more information or any questions regarding Pretrained Models and Fine-tuned Models, please don't hesitate to contact us at
Email: info@geowgs84.com
USA (HQ): (720) 702–4849
(A GeoWGS84 Corp Company)
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