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The data distributions of four synthetic datasets (S1.3, S2.3, S3.3, and S4.3) generated from the stratified sampling method with í µí¼ í µí±ší µí±Ží µí±¥ = 64. We can see that when í µí± í µí¼‡ increases, a more fine-grain stratified sampling is carried out. The resulting dataset has a larger data bias toward the polar areas.

The data distributions of four synthetic datasets (S1.3, S2.3, S3.3, and S4.3) generated from the stratified sampling method with í µí¼ í µí±ší µí±Ží µí±¥ = 64. We can see that when í µí± í µí¼‡ increases, a more fine-grain stratified sampling is carried out. The resulting dataset has a larger data bias toward the polar areas.

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Figure 2: Applying location encoders to differentiate two visually...
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Figure 5: The data distributions of four synthetic datasets (U1, U2,...
Table 5
+7 Figure 6: The data distributions of four synthetic datasets (S1.3,...
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions
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  • Jun 2023
Generating learning-friendly representations for points in space is a fundamental and long-standing problem in ML. Recently, multi-scale encoding schemes (such as Space2Vec and NeRF) were proposed to directly encode any point in 2D/3D Euclidean space as a high-dimensional vector, and has been successfully applied to various geospatial prediction an...
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... this kind of systematic bias can be avoided if we use a spherical location encoder as Sphere2Vec. Figure 6 visualizes the data distributions of four synthetic datasets with stratified sampling method. They have different í µí± í µí¼‡ but the same í µí¼ í µí±ší µí±Ží µí±¥ . ...

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