README.md

metadata

license: apache-2.0
tags:
  - location-encoder
  - geospatial
  - remote-sensing
  - siren
  - implicit-neural-representation
library_name: pytorch

HydroLoc

A ReSIREN location encoder that maps a geographic coordinate (lat, lon) to a 512-d embedding. It is a MIND-style model (residual SIREN trunk over an Equal-Earth projection of the coordinate) trained by distilling two frozen image foundation models evaluated over ~106k global Sentinel-2 water patches from the Hydro dataset:

• DINOv3 ViT-L/16 (vit_large_patch16_dinov3, timm) on the RGB quicklooks → 1024-d
• OlmoEarth v1.2 Base on the 12-band multispectral tiles → 768-d

The trunk is supervised with a Matryoshka objective (nested prefixes 64/128/256/512), so any leading slice embedding[:, :m] is itself a usable, compact location embedding — the signal is front-loaded into the earliest dimensions.

Usage

import torch
from hydroloc import HydroLoc

model = HydroLoc.from_pretrained("isaaccorley/hydroloc").eval()

latlon = torch.tensor([[37.77, -122.42],   # (lat, lon) in degrees
                       [-8.70, 45.00]])
with torch.no_grad():
    emb = model(latlon)        # [2, 512]
    emb64 = emb[:, :64]        # compact 64-d Matryoshka prefix

hydroloc.py is self-contained and depends only on torch (plus huggingface_hub and safetensors for from_pretrained).

Coordinate convention

Input is (..., 2) with column 0 = latitude, column 1 = longitude, in degrees. Internally the coordinate is mapped through the Equal-Earth projection before the SIREN trunk.

Files

• hydroloc.py — standalone model definition + loader
• model.safetensors — trunk weights
• config.json — architecture config

Architecture

Trunk	residual SIREN, embed_dim=512, depth=4, w0_first=30
Input	Equal-Earth-projected (lat, lon)
Output	512-d embedding; Matryoshka prefixes [64, 128, 256, 512]
Objective	cosine + MSE distillation of L2-normalized teacher embeddings
Teachers	DINOv3 ViT-L/16 (RGB), OlmoEarth v1.2 Base (multispectral)

The per-teacher distillation heads are training-only and not included; the released artifact is the coordinate → embedding trunk.

Notes

• Trained on water locations, so embeddings are most meaningful over oceans/coasts/inland water.
• The embedding is spatially smooth (nearby coordinates → similar embeddings) and, under PCA, recovers global coastline structure.