IEEE Geoscience and Remote Sensing Magazine - September 2023 - 90

using a user-defined callback system to improve
the flexibility of data management. Here, a simple
demo code using the SpatialDataGenerator
class from Keras Spatial to prepare a training set for a
DL model is given:
1 from keras _ spatial import SpatialDataGenerator
2
3
# Loading labels from a local file
4 labels = SpatialDataGenerator()
5 labels.source = '/path/to/labels.tif'
6 # Sample 128 x 128 patches
7 labels.width, labels.height = 128, 128
8 # Set a geodataframe with 200 x 200
9 # in projection units of the original raster
10 df = labels.regular _ grid(200,200)
11
12 # Loading images from a file on the cloud
13 samples = SpatialDataGenerator()
14 samples.source = 'https://server.com/
files/data.tif'
15 samples.width, samples.height = labels.
width, label.height
16
17 # The training set generator
18 train _ gen =
dataframe(df),
dataframe(df))
19
20 # Train a model
21 model(train _ gen)
GEOSPATIAL COMPUTING PLATFORMS
In addition to ML libraries, public geospatial computing
platforms such as Google Earth Engine (GEE) for EO tasks
in practical application scenarios offer a series of benefits,
including the following:
◗ Access to large-scale datasets: geospatial computing platforms
usually provide access to large and diverse geospatial datasets,
such as satellite imagery, weather data, and terrain data.
zip(labels.flow _ from _
patches.flow _ from _
These datasets may be time consuming and expensive to
acquire on one's own but can be easily accessed through a
geospatial computing platform using the cloud service.
◗ Scalability: geospatial computing platforms are designed
to address large-scale geospatial data processing and
analysis. They usually provide cloud-based computing
resources that can be easily scaled up or down to meet researchers'
needs. This makes it easier to perform complex
geospatial analysis that would be difficult to do on local
machines considering the limited computing resources.
◗ Prebuilt tools and APIs: geospatial computing platforms usually
provide prebuilt tools and programming APIs for image
processing, geocoding, and data visualization, making
it much easier for researchers to work with geospatial data.
Several representative geospatial computing platforms are
as follows:
◗ GEE [76] is a cloud-based platform designed for largescale
geospatial data analysis and processing. It provides
a range of tools and APIs that allow users to analyze and
visualize geospatial data, including raster and vector
processing tools, ML algorithms, and geospatial modeling
tools. In addition, GEE provides access to powerful
computing resources, such as virtual machines and
storage, to enable users to perform complex geospatial
analyses. The GEE Data Catalog contains more than
40 years of historical imagery and scientific datasets for
Earth science, which are updated and expanded daily.
These datasets cover various topics such as climate,
weather, surface temperature, terrain, and land cover.
Notable datasets available on the GEE Data Catalog include
Planet SkySat Public Ortho Imagery (collected for
crisis response events) [42] and NAIP [77] (agricultural
monitoring data in the United States).
◗ AWS [78] is a powerful platform for geospatial computing
that offers a range of services for geospatial data storage,
processing, and analysis. AWS hosts several representative
geospatial datasets, including Digital Earth Africa
[79] (Landsat and Sentinel products over Africa), National
Oceanic and Atmospheric Administration Emergency
Response Imagery [80] (lidar and hyperspectral data
over the United States), and datasets from the SpaceNet
Deployment
Inputs
Labels
Images
AOI
ANALYZE CHIP
Dataset
Metrics
Training
Chips
RasterVision Pipeline
Batch
TRAIN PREDICT EVAL BUNDLE
Training
Model
Validation
Predictions
Evaluation
Metrics
Model
Bundle
Custom
Integrations
FIGURE 27. An illustration of the pipeline from the RasterVision package. AOI: area of interest. (Source: https://github.com/azavea/raster-vision.)
90
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2023
Predictions
Live
Predictions
https://github.com/azavea/raster-vision
IEEE Geoscience and Remote Sensing Magazine - September 2023

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