Datasets:
Tasks:
Image-to-3D
Modalities:
Geospatial
Languages:
English
Size:
100K<n<1M
Tags:
3d-point-cloud
point-cloud-generation
city-scale
remote-sensing
satellite-imagery
digital-surface-model
License:
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| Sanity-check the coordinates and CRS of georeferenced LAS/LAZ point clouds. | |
| Role in the pipeline: | |
| A verification utility that confirms the HoliCity (London) tiles have been | |
| georeferenced correctly before tiling. It inspects each LAS/LAZ file's header | |
| and bounding box, reprojects the bbox center to WGS84, and checks whether that | |
| center falls within an approximate London bounding box. | |
| Behavior: | |
| - Recursively scans a directory for .las/.laz files. | |
| - Reads header stats (EPSG, bbox, point count, point format, scale, offset) | |
| and detects degenerate "near (0,0)" coordinates. | |
| - If the file has an EPSG, reprojects its center to WGS84 and flags whether it | |
| lies in the London region. | |
| - If no EPSG is present, tries a list of candidate CRSs (BNG / UTM 30N / Web | |
| Mercator / WGS84) and reports the first one whose center lands in London. | |
| - Emits a per-file status (OK / WARN / BAD / ERR) plus a hint, printed as a | |
| table and optionally exported to CSV. | |
| Inputs: directory of .las/.laz files (CLI: -d/--dir), optional CSV path (-o/--output). | |
| Outputs: a console report table and an optional CSV file. No files are modified. | |
| External tools: laspy, numpy, pyproj. | |
| """ | |
| import os | |
| import sys | |
| import csv | |
| import math | |
| import argparse | |
| from pathlib import Path | |
| import laspy | |
| import numpy as np | |
| from pyproj import CRS, Transformer | |
| # ----------- Tunable parameters ----------- | |
| # London region (WGS84) | |
| LON_MIN, LON_MAX = -0.6, 0.4 | |
| LAT_MIN, LAT_MAX = 51.2, 51.8 | |
| # Common candidate CRSs (used to guess when no EPSG is present) | |
| CANDIDATE_EPSGS = [ | |
| 27700, # OSGB36 / British National Grid | |
| 32630, # WGS84 / UTM zone 30N | |
| 3857, # Web Mercator | |
| 4326, # WGS84 (lat/lon) | |
| ] | |
| # Rough "plausible ranges" for the UK / London (quick sanity check; approximate only) | |
| RANGE_HINTS = { | |
| 27700: {"E": (0, 700000), "N": (0, 1300000), "name": "OSGB36 / BNG"}, | |
| 32630: {"E": (160000, 840000), "N": (5550000, 5900000), "name": "UTM 30N"}, | |
| 3857: {"X": (-500000, 500000), "Y": (6200000, 7300000), "name": "WebMerc"}, | |
| 4326: {"Lon": (-10, 10), "Lat": (45, 60), "name": "WGS84 deg"}, | |
| } | |
| # Approximate reference for the center of London (used only for printed hints) | |
| LONDON_WGS84 = (-0.1, 51.51) | |
| # -------------------------------- | |
| def in_london(lon, lat): | |
| return (LON_MIN <= lon <= LON_MAX) and (LAT_MIN <= lat <= LAT_MAX) | |
| def safe_epsg_str(epsg): | |
| try: | |
| return f"EPSG:{int(epsg)}" | |
| except Exception: | |
| return "None" | |
| def read_stats(path: Path, sample_n: int = 200000): | |
| """Read the bbox and center point; for large clouds only the header bbox is read; sample some points for QC if needed.""" | |
| with laspy.open(str(path)) as f: | |
| hdr = f.header | |
| mins = np.array(getattr(hdr, "mins", getattr(hdr, "min", (0, 0, 0))), dtype=float) | |
| maxs = np.array(getattr(hdr, "maxs", getattr(hdr, "max", (0, 0, 0))), dtype=float) | |
| epsg = None | |
| try: | |
| epsg = hdr.epsg | |
| except Exception: | |
| pass | |
| # Center point (the bbox midpoint is sufficient) | |
| cx = (mins[0] + maxs[0]) * 0.5 | |
| cy = (mins[1] + maxs[1]) * 0.5 | |
| cz = (mins[2] + maxs[2]) * 0.5 | |
| # Check whether everything sits near (0,0) | |
| zeroish = (abs(cx) < 1e-6 and abs(cy) < 1e-6) or \ | |
| (abs(mins[0]) < 1e-6 and abs(maxs[0]) < 1e-6 and | |
| abs(mins[1]) < 1e-6 and abs(maxs[1]) < 1e-6) | |
| return { | |
| "epsg": epsg, | |
| "mins": mins, "maxs": maxs, | |
| "center": (cx, cy, cz), | |
| "zeroish": zeroish, | |
| "point_count": int(getattr(hdr, "point_count", 0)), | |
| "point_format": str(getattr(hdr.point_format, "id", hdr.point_format)), | |
| "scale": tuple(hdr.scales), | |
| "offset": tuple(hdr.offsets), | |
| } | |
| def transform_to_wgs84(x, y, epsg): | |
| """Reproject (x,y) from the given EPSG to WGS84 lon/lat. Return None on failure.""" | |
| try: | |
| src = CRS.from_epsg(int(epsg)) | |
| dst = CRS.from_epsg(4326) | |
| tr = Transformer.from_crs(src, dst, always_xy=True) | |
| lon, lat = tr.transform(x, y) | |
| return lon, lat | |
| except Exception: | |
| return None | |
| def guess_and_transform_to_wgs84(x, y, candidates=CANDIDATE_EPSGS): | |
| """When no EPSG is set, try each candidate CRS in turn; return the first projection that falls within the London region, along with its epsg.""" | |
| tried = [] | |
| for epsg in candidates: | |
| res = transform_to_wgs84(x, y, epsg) | |
| if res is None: | |
| tried.append((epsg, None)) | |
| continue | |
| lon, lat = res | |
| tried.append((epsg, (lon, lat))) | |
| if in_london(lon, lat): | |
| return (lon, lat), epsg, tried | |
| return None, None, tried | |
| def range_hint_text(epsg, mins, maxs): | |
| h = RANGE_HINTS.get(int(epsg)) if epsg is not None else None | |
| if not h: | |
| return "" | |
| if epsg in (27700, 32630): | |
| E = (mins[0], maxs[0]); N = (mins[1], maxs[1]) | |
| return f"RangeHint {h['name']}: E∈{h['E']} vs {E}, N∈{h['N']} vs {N}" | |
| elif epsg == 3857: | |
| X = (mins[0], maxs[0]); Y = (mins[1], maxs[1]) | |
| return f"RangeHint {h['name']}: X∈{h['X']} vs {X}, Y∈{h['Y']} vs {Y}" | |
| elif epsg == 4326: | |
| Lon = (mins[0], maxs[0]); Lat = (mins[1], maxs[1]) | |
| return f"RangeHint {h['name']}: Lon∈{h['Lon']} vs {Lon}, Lat∈{h['Lat']} vs {Lat}" | |
| return "" | |
| def analyze_file(path: Path): | |
| size_mb = path.stat().st_size / (1024 * 1024) | |
| stats = read_stats(path) | |
| epsg = stats["epsg"] | |
| cx, cy, cz = stats["center"] | |
| mins, maxs = stats["mins"], stats["maxs"] | |
| result = { | |
| "file": str(path.name), | |
| "size_mb": f"{size_mb:.2f}", | |
| "epsg": safe_epsg_str(epsg), | |
| "pt_fmt": stats["point_format"], | |
| "pts": stats["point_count"], | |
| "scale": stats["scale"], | |
| "offset": stats["offset"], | |
| "center_xy": (cx, cy), | |
| "center_wgs84": None, | |
| "in_london": False, | |
| "status": "", | |
| "hint": "", | |
| } | |
| # 0) All-zero / near-zero | |
| if stats["zeroish"]: | |
| result["status"] = "BAD" | |
| result["hint"] = "Coordinates near (0,0); likely unassigned or wrong projection. Check the coordinate transform and EPSG write." | |
| return result | |
| # 1) Has EPSG: project and check directly | |
| if epsg is not None: | |
| wgs = transform_to_wgs84(cx, cy, int(epsg)) | |
| if wgs is None: | |
| result["status"] = "WARN" | |
| result["hint"] = f"Could not project the center from {safe_epsg_str(epsg)} to WGS84; the EPSG may be invalid." | |
| return result | |
| lon, lat = wgs | |
| result["center_wgs84"] = (round(lon, 6), round(lat, 6)) | |
| result["in_london"] = in_london(lon, lat) | |
| if result["in_london"]: | |
| result["status"] = "OK" | |
| result["hint"] = f"Center is within the London region; {range_hint_text(int(epsg), mins, maxs)}" | |
| else: | |
| result["status"] = "WARN" | |
| result["hint"] = f"Center is outside the London region ({lon:.5f},{lat:.5f}); if it should be in London, the EPSG or translation may be wrong. {range_hint_text(int(epsg), mins, maxs)}" | |
| return result | |
| # 2) No EPSG: try to guess and check whether it lands in London | |
| guessed, gepsg, tried = guess_and_transform_to_wgs84(cx, cy) | |
| if guessed is not None: | |
| lon, lat = guessed | |
| result["center_wgs84"] = (round(lon, 6), round(lat, 6)) | |
| result["in_london"] = True | |
| result["status"] = "WARN" | |
| result["hint"] = (f"No EPSG written, but {safe_epsg_str(gepsg)} is inferred to fall in the London region. " | |
| f"Suggest writing {safe_epsg_str(gepsg)} and retrying.") | |
| else: | |
| result["status"] = "BAD" | |
| tried_text = "; ".join( | |
| f"EPSG:{e} -> {('None' if v is None else f'({v[0]:.5f},{v[1]:.5f})')}" for e, v in tried | |
| ) | |
| result["hint"] = ("No EPSG written, and none of the common candidate CRSs project the center into the London region. " | |
| "Check whether a wrong translation/rotation/unit was used, or whether a different EPSG is needed. " | |
| f"Attempts: {tried_text}") | |
| return result | |
| def print_table(rows): | |
| headers = ["file","size_mb","epsg","pt_fmt","pts","center_xy","center_wgs84","in_london","status","hint"] | |
| colw = {h: max(len(h), max((len(str(r[h])) for r in rows), default=0)) for h in headers} | |
| sep = " | " | |
| print(sep.join(h.ljust(colw[h]) for h in headers)) | |
| print("-" * (sum(colw.values()) + len(sep)*(len(headers)-1))) | |
| for r in rows: | |
| print(sep.join(str(r[h]).ljust(colw[h]) for h in headers)) | |
| def main(): | |
| ap = argparse.ArgumentParser(description="Check whether LAS/LAZ files have correct coordinates and CRS, and whether they fall within the London region.") | |
| ap.add_argument("-d","--dir", default=".", help="Directory to scan (default: current directory)") | |
| ap.add_argument("-o","--output", default=None, help="CSV export path (optional)") | |
| args = ap.parse_args() | |
| root = Path(args.dir).resolve() | |
| if not root.exists(): | |
| print(f"Directory does not exist: {root}", file=sys.stderr); sys.exit(1) | |
| files = [] | |
| for p in root.rglob("*"): | |
| if p.is_file() and p.suffix.lower() in (".las",".laz"): | |
| files.append(p) | |
| if not files: | |
| print("No .las/.laz files found"); return | |
| rows = [] | |
| for p in sorted(files): | |
| try: | |
| rows.append(analyze_file(p)) | |
| except Exception as e: | |
| rows.append({ | |
| "file": p.name, "size_mb":"?", "epsg":"?", "pt_fmt":"?", "pts":"?", | |
| "center_xy":"?", "center_wgs84":"?", "in_london":"?", "status":"ERR", | |
| "hint": f"Parse failed: {e}" | |
| }) | |
| print(f"Scan directory: {root}\n") | |
| print_table(rows) | |
| if args.output: | |
| out = Path(args.output).resolve() | |
| out.parent.mkdir(parents=True, exist_ok=True) | |
| with out.open("w", newline="", encoding="utf-8") as f: | |
| writer = csv.DictWriter(f, fieldnames=list(rows[0].keys())) | |
| writer.writeheader() | |
| writer.writerows(rows) | |
| print(f"\nCSV written: {out}") | |
| if __name__ == "__main__": | |
| main() | |