Merge pull request #174 from bacetiner/master

BRAILS can now create a baseline building inventory from NSI building data

brails/workflow/NSIParser.py

@@ -0,0 +1,147 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 The Regents of the University of California
+#
+# This file is part of BRAILS.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice,
+# this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its contributors
+# may be used to endorse or promote products derived from this software without
+# specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#
+# You should have received a copy of the BSD 3-Clause License along with
+# BRAILS. If not, see <http://www.opensource.org/licenses/>.
+#
+# Contributors:
+# Barbaros Cetiner
+#
+# Last updated:
+# 01-18-2024  
+
+import requests 
+import numpy as np
+import pandas as pd
+import os
+from requests.adapters import HTTPAdapter, Retry
+from shapely.strtree import STRtree
+from shapely import Polygon, Point
+
+class NSIParser:
+    def __init__(self): 
+        self.inventory = []
+
+    def GenerateBldgInventory(self,footprints):
+        minlat = footprints[0][0][1]
+        minlon = footprints[0][0][0]
+        maxlat = footprints[0][0][1]
+        maxlon = footprints[0][0][0]
+        for fp in footprints:
+            for vert in fp:
+                if vert[1]>maxlat:
+                    maxlat = vert[1]
+                if vert[0]>maxlon:
+                    maxlon = vert[0]
+                if vert[1]<minlat:
+                    minlat = vert[1]
+                if vert[0]<minlon:
+                    minlon = vert[0]
+
+        bboxstr = f'{minlon:.5f},{minlat:.5f},{minlon:.5f},{maxlat:.5f},{maxlon:.5f},{maxlat:.5f},{maxlon:.5f},{minlat:.5f},{minlon:.5f},{minlat:.5f}'
+
+        url = "https://nsi.sec.usace.army.mil/nsiapi/structures?bbox="
+
+        # Getting the NSI building points for the 
+        # Define a retry stratey fgor common error codes to use when
+        # downloading tiles:
+        s = requests.Session()
+        retries = Retry(total=5, 
+                        backoff_factor=0.1,
+                        status_forcelist=[500, 502, 503, 504])
+        s.mount('https://', HTTPAdapter(max_retries=retries))
+
+        # Download tile using the defined retry strategy:
+        print('\nGetting National Structure Inventory (NSI) building points for the building footprints...')
+        response = s.get(url+bboxstr)       
+        datalist = response.json()['features']
+
+        datadict = {}
+        count = 0
+        for data in datalist:
+            pt = Point(data['geometry']['coordinates'])
+            if pt in datadict.keys():
+                count+=1
+            datadict[pt] = data['properties']
+        points = list(datadict.keys())
+        pttree = STRtree(points)
+
+        ress = []
+        for fp in footprints:
+            res = pttree.query(Polygon(fp))
+            if res.size!=0:
+                ress.append(res)
+            else:
+                ress.append(np.empty(shape=(0, 0)))
+
+        footprints_out = []
+        lat = []
+        lon = []
+        planarea = []
+        nstories = []
+        yearbuilt = []
+        repcost = []
+        strtype = []
+        occ = []
+        for ind, fp in enumerate(footprints):
+            if ress[ind].size!=0:
+                footprints_out.append(('{"type":"Feature","geometry":' + 
+                '{"type":"Polygon","coordinates":[' + 
+                f"""{fp}""" + 
+                ']},"properties":{}}'))
+                ptind = ress[ind][0]
+                ptres = datadict[points[ptind]]
+                lat.append(ptres['y'])
+                lon.append(ptres['x'])
+                planarea.append(ptres['sqft'])
+                nstories.append(ptres['num_story'])
+                yearbuilt.append(ptres['med_yr_blt'])
+                repcost.append(ptres['val_struct'])
+                strtype.append(ptres['bldgtype'])
+                occ.append(ptres['occtype'])
+
+        print(f'Found a total of {len(footprints_out)} building points in NSI that match the footprint data.')
+
+        inventory = pd.DataFrame(pd.Series(lat,name='Latitude'))
+        inventory['Longitude'] = lon
+        inventory['PlanArea'] = planarea
+        inventory['NumberOfStories'] = nstories
+        inventory['YearBuilt'] = yearbuilt
+        inventory['ReplacementCost'] = repcost
+        inventory['StructureType'] = strtype
+        inventory['OccupancyClass'] = occ
+        inventory['Footprint'] = footprints_out
+
+        self.inventory= inventory
+
+        inventory.to_csv('inventory.csv', index=True, index_label='id') 
+        print(f'\nFinal inventory data available in {os.getcwd()}/inventory.csv')