--- name: olmoearth-mlx-bb296c26e8f1 description: "OlmoEarth MLX: Spatio-Temporal Earth Intelligence" version: 1.0.0 --- # OlmoEarth MLX: Spatio-Temporal Earth Intelligence **Trit**: +1 (PLUS - world creation via planetary observation) **Foundation**: AI2 OlmoEarth + Apple MLX + GeoACSet + Dune.xyz Geographic WEV ## Overview OlmoEarth is AI2's open spatio-temporal foundation model for planetary intelligence, trained on: - Sentinel-2 L2A (12 bands, 10-60m resolution) - Sentinel-1 SAR (VV, VH polarization) - Landsat (historical continuity) - WorldCover (11 land cover classes) - OpenStreetMap raster - SRTM elevation - WRI Canopy Height Map This skill enables: 1. **Geographic embedding** of crypto wallet activity from Dune.xyz 2. **Impact area identification** for Protocol Labs infrastructure 3. **GeoACSet materialization** for categorical spatial reasoning ## Architecture ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ OLMOEARTH-MLX PIPELINE │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ Dune.xyz Query ─┬─► IP Geolocation ─┬─► OlmoEarth Embedding │ │ │ │ │ │ Wallet Activity ┘ Region Bounds ─┘ FlexiVit Encoder │ │ │ │ │ ▼ │ │ ┌───────────────────────────────────┐ │ │ │ GeoACSet Materialization │ │ │ │ Regions → Districts → Parcels │ │ │ │ with GF(3) trits │ │ │ └───────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌───────────────────────────────────┐ │ │ │ Tenderloin WEV Integration │ │ │ │ Geographic Value Extraction │ │ │ └───────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────────┘ ``` ## OlmoEarth Model Specs | Model | Encoder Params | Decoder Params | Embed Dim | Depth | |-------|----------------|----------------|-----------|-------| | Nano | 5.8M | 3.7M | 192 | 12 | | Tiny | 22M | 14M | 384 | 12 | | Base | 86M | 55M | 768 | 12 | | Large | 307M | 197M | 1024 | 24 | ### Supported Modalities ```python MODALITIES = { "SENTINEL2_L2A": { "bands": ["B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12", "SCL", "CLD"], "gsd": [10, 10, 10, 20, 20, 20, 10, 20, 20, 20, 20, 20], "temporal": True }, "SENTINEL1": { "bands": ["VV", "VH"], "gsd": [10, 10], "temporal": True }, "WORLDCOVER": { "bands": ["LC"], "classes": 11, # Tree, Shrub, Grass, Crop, Built, Bare, Snow, Water, Wetland, Mangrove, Moss "gsd": [10], "temporal": False }, "SRTM": { "bands": ["elevation"], "gsd": [30], "temporal": False } } ``` ## MLX Inference ```python # olmoearth_mlx.py - Apple Silicon optimized inference import mlx.core as mx import mlx.nn as nn from dataclasses import dataclass from typing import Dict, List, Optional, Tuple @dataclass class OlmoEarthConfig: """OlmoEarth model configuration for MLX.""" embed_dim: int = 768 depth: int = 12 num_heads: int = 12 patch_size: int = 4 num_modalities: int = 8 temporal_tokens: int = 4 class FlexiVitEncoder(nn.Module): """FlexiVit encoder for OlmoEarth on MLX.""" def __init__(self, config: OlmoEarthConfig): super().__init__() self.config = config # Patch embeddings per modality self.patch_embed = nn.Linear( config.patch_size * config.patch_size * 12, # Max bands config.embed_dim ) # Positional encoding self.pos_embed = mx.zeros((1, 256, config.embed_dim)) # Transformer blocks self.blocks = [ TransformerBlock(config.embed_dim, config.num_heads) for _ in range(config.depth) ] self.norm = nn.LayerNorm(config.embed_dim) def __call__(self, x: mx.array, timestamps: mx.array) -> mx.array: # Patchify and embed x = self.patch_embed(x) x = x + self.pos_embed[:, :x.shape[1], :] # Transform for block in self.blocks: x = block(x) return self.norm(x) class TransformerBlock(nn.Module): def __init__(self, dim: int, num_heads: int): super().__init__() self.attn = nn.MultiHeadAttention(dim, num_heads) self.mlp = nn.Sequential( nn.Linear(dim, dim * 4), nn.GELU(), nn.Linear(dim * 4, dim) ) self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) def __call__(self, x: mx.array) -> mx.array: x = x + self.attn(self.norm1(x), self.norm1(x), self.norm1(x)) x = x + self.mlp(self.norm2(x)) return x ``` ## Dune.xyz Geographic Mapping ### Trackable Interactions | Query | Geographic Signal | GeoACSet Mapping | |-------|-------------------|------------------| | Filecoin Storage Deals | Provider location | Region → District | | FVM Contract Deploys | Creator wallet region | District → Parcel | | Bridge Transactions | Source/dest chains | Region ↔ Region | | Grant Disbursements | Recipient geography | Parcel → Building | ### SQL Queries (Dune) ```sql -- Filecoin storage provider geographic distribution SELECT provider_id, COALESCE(ip_geolocation.country, 'Unknown') as country, COALESCE(ip_geolocation.region, 'Unknown') as region, COUNT(*) as deals, SUM(piece_size) as total_bytes FROM filecoin.storage_deals sd LEFT JOIN ip_geolocation ON sd.provider_ip = ip_geolocation.ip WHERE deal_timestamp > NOW() - INTERVAL '30 days' GROUP BY 1, 2, 3 ORDER BY deals DESC; -- FVM contract geographic heat map SELECT DATE_TRUNC('day', block_timestamp) as day, creator_country, creator_region, COUNT(DISTINCT contract_address) as contracts, SUM(gas_used) as total_gas FROM filecoin.fvm_contracts WHERE block_timestamp > NOW() - INTERVAL '90 days' GROUP BY 1, 2, 3; -- Cross-chain bridge flows by geography SELECT source_chain, dest_chain, sender_region, receiver_region, SUM(amount_usd) as volume_usd, COUNT(*) as tx_count FROM bridge_transactions WHERE timestamp > NOW() - INTERVAL '7 days' AND (source_chain = 'filecoin' OR dest_chain = 'filecoin') GROUP BY 1, 2, 3, 4; ``` ## GeoACSet Materialization ```julia # GeoACSet schema for crypto-geographic data @present SchCryptoGeo(FreeSchema) begin # Objects Region::Ob District::Ob Wallet::Ob Transaction::Ob EarthTile::Ob # Morphisms region_of::Hom(District, Region) district_of::Hom(Wallet, District) sender::Hom(Transaction, Wallet) receiver::Hom(Transaction, Wallet) tile_of::Hom(Wallet, EarthTile) # Attributes lat::Attr(Region, Float64) lon::Attr(Region, Float64) embedding::Attr(EarthTile, Vector{Float32}) # OlmoEarth embedding trit::Attr(Transaction, Int) # GF(3) classification end @acset_type CryptoGeoACSet(SchCryptoGeo) # Materialization from Dune query results function materialize_crypto_geo(dune_results::DataFrame)::CryptoGeoACSet acset = CryptoGeoACSet() # Add regions for row in eachrow(unique(dune_results, :region)) add_part!(acset, :Region; lat=row.lat, lon=row.lon ) end # Add districts within regions for row in eachrow(unique(dune_results, [:region, :district])) region_id = findfirst(r -> r == row.region, acset[:Region]) add_part!(acset, :District; region_of=region_id) end # Add wallets with earth tile embeddings for row in eachrow(unique(dune_results, :wallet)) district_id = findfirst(d -> d == row.district, acset[:District]) tile_embedding = get_olmoearth_embedding(row.lat, row.lon) tile_id = add_part!(acset, :EarthTile; embedding=tile_embedding) add_part!(acset, :Wallet; district_of=district_id, tile_of=tile_id) end return acset end ``` ## Impact Area Identification ### Protocol Labs Infrastructure Zones ```python PL_IMPACT_ZONES = { "high_density": [ {"name": "SF Bay Area", "lat": 37.7749, "lon": -122.4194, "radius_km": 100}, {"name": "Greater NYC", "lat": 40.7128, "lon": -74.0060, "radius_km": 80}, {"name": "Singapore", "lat": 1.3521, "lon": 103.8198, "radius_km": 50}, ], "emerging": [ {"name": "Shenzhen", "lat": 22.5431, "lon": 114.0579, "radius_km": 60}, {"name": "Berlin", "lat": 52.5200, "lon": 13.4050, "radius_km": 40}, {"name": "Bangalore", "lat": 12.9716, "lon": 77.5946, "radius_km": 50}, ], "frontier": [ {"name": "Lagos", "lat": 6.5244, "lon": 3.3792, "radius_km": 30}, {"name": "São Paulo", "lat": -23.5505, "lon": -46.6333, "radius_km": 60}, {"name": "Jakarta", "lat": -6.2088, "lon": 106.8456, "radius_km": 40}, ] } def classify_impact_zone(lat: float, lon: float) -> Tuple[str, str, float]: """Classify a coordinate into PL impact zone.""" from math import radians, sin, cos, sqrt, atan2 def haversine(lat1, lon1, lat2, lon2): R = 6371 # Earth radius in km dlat = radians(lat2 - lat1) dlon = radians(lon2 - lon1) a = sin(dlat/2)**2 + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2)**2 return 2 * R * atan2(sqrt(a), sqrt(1-a)) for zone_type, zones in PL_IMPACT_ZONES.items(): for zone in zones: dist = haversine(lat, lon, zone["lat"], zone["lon"]) if dist <= zone["radius_km"]: return zone_type, zone["name"], dist return "global", "Other", float("inf") ``` ## GF(3) Geographic Classification | Trit | Zone Type | WEV Signal | Strategy | |------|-----------|------------|----------| | +1 | high_density | Infrastructure concentration | Long storage deals | | 0 | emerging | Growth potential | Bridge liquidity | | -1 | frontier | Adoption catalyst | Grant allocation | ## Tenderloin Integration ```python # Connect OlmoEarth embeddings to Tenderloin WEV def geo_wev_extraction( dune_query: str, olmoearth_model: FlexiVitEncoder, geoacset: CryptoGeoACSet ) -> List[Dict]: """Extract geographic WEV from Dune data with OlmoEarth embeddings.""" # Execute Dune query results = execute_dune_query(dune_query) wev_events = [] for row in results: # Get OlmoEarth embedding for location embedding = olmoearth_model.embed(row.lat, row.lon) # Classify impact zone zone_type, zone_name, dist = classify_impact_zone(row.lat, row.lon) # Map to GF(3) trit trit = {"high_density": 1, "emerging": 0, "frontier": -1}.get(zone_type, 0) wev_events.append({ "source": "dune_geo", "lat": row.lat, "lon": row.lon, "zone": zone_name, "trit": trit, "embedding": embedding.tolist(), "value": row.volume_usd, "category": "geographic" }) return wev_events ``` ## Usage ```bash # Install OlmoEarth pip install olmoearth-pretrain # Run geographic WEV extraction python -c " from olmoearth_mlx import OlmoEarthMLX from tenderloin.fund_wallets import WalletFundingEngine # Load model model = OlmoEarthMLX.load('olmoearth-base') # Query Dune for geographic data geo_wev = model.extract_geographic_wev( query='filecoin_storage_providers_by_region', timeframe='30d' ) # Fund wallets based on geographic WEV engine = WalletFundingEngine() for event in geo_wev: engine.ingest_wev(event['category'], event['value'], trit=event['trit']) " ``` ## Canonical Triads ``` olmoearth-mlx (+1) ⊗ geoacset (0) ⊗ dune-geographic (-1) = 0 ✓ tenderloin (+1) ⊗ prediction-markets (0) ⊗ olmoearth-mlx (-1) = 0 ✓ ``` ## References - [OlmoEarth arXiv Paper](https://arxiv.org/abs/2411.xxxxx) - [allenai/olmoearth_pretrain](https://github.com/allenai/olmoearth_pretrain) - [bmorphism/GeoACSets.jl](https://github.com/bmorphism/GeoACSets.jl) - [Dune Analytics](https://dune.com) - [Filecoin Data Portal](https://filecoindataportal.xyz) ## Scientific Skill Interleaving This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem: ### Autodiff - **jax** [○] via bicomodule - Hub for autodiff/ML ### Bibliography References - `general`: 734 citations in bib.duckdb ## Cat# Integration This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure: ``` Trit: 0 (ERGODIC) Home: Prof Poly Op: ⊗ Kan Role: Adj Color: #26D826 ``` ### GF(3) Naturality The skill participates in triads satisfying: ``` (-1) + (0) + (+1) ≡ 0 (mod 3) ``` This ensures compositional coherence in the Cat# equipment structure. --- Source attribution: plurigrid Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/plugins/asi/skills/olmoearth-mlx/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 SkillCreator.ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: plurigrid Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/plugins/asi/skills/olmoearth-mlx/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Data Scientist, ML / AI Engineer, Product Manager Specialties: API, Backend Tools: Apple Mlx Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 8897c5630b3b6bd7e4abd7098322cab6e05ef2ab21e9941ca796707040ddf2d0