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版權(quán)所有 ? 2011 山東大學(xué)附屬中學(xué)
ICP 備案號(hào)：魯ICP備05001952號(hào)-13
學(xué)校地址：山東省濟(jì)南市洪家樓北路177號(hào)
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版權(quán)所有 ? 2011 山東大學(xué)附屬中學(xué)
ICP 備案號(hào)：魯ICP備05001952號(hào)-13
學(xué)校地址：山東省濟(jì)南市洪家樓北路177號(hào)
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About SDFD | City of San Diego Official Website
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Type
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Home
Leisure
Leisure
With its great weather, miles of sandy beaches, and major attractions, San Diego is known worldwide as one of the best tourist destinations and a great place for residents to relax year round.
Parks
Regional Parks
Community Parks
Recreation Centers
Book an Event
Outdoors
Beaches & Bays
Golf
More Leisure
Balboa Park
Torrey Pines Golf Course
Special Events & Filming
Arts & Culture
Resident Resources
Resident Resources
From neighborhoods and parks to streets and parking, find what you need in your community and report your concerns.
Neighborhoods
Community Plans
Recreational Activities
Pools
Street Maintenance
Potholes
Street Sweeping
Streetlights
More Resident Resources
View/Pay Water Bill
View Trash Collection Schedule
Parking Citations
City Strategic Plan
Doing Business
Doing Business
Incentives and programs are available to help launch, grow and expand your business, and provide support for homeowners and contractors to get work done.
Plan
Bid on a Contract
Explore Incentives
Development Codes & Regulations
Fix
Set Up Utilities
Renovations & Remodels
Build
Get Permits
Track Your Project
Learn about Zoning
Business Tax Certificate
Ten Key Steps to Starting Your Business
Go Green
Library
Library
The San Diego Public Library is a popular destination that connects our diverse community to free educational and cultural resources that will enrich their lives.
Programs & Events
One Book, One San Diego
Event Calendar
More Programs & Events
Services
Fines & Fees
Renewals & Holds
More Services
Kids & Teens
Homework Help Center
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More Kids & Teens
eCollection
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Digital Magazines
Library Website
Browse the Catalog
Apply for a Library Card
Volunteer
Employment Opportunities
Public Safety
Public Safety
The Police and Fire-Rescue departments work together within our communities to provide the highest level of quality service and protection. From neighborhood watch to 9-1-1 services, our team is here for you 24 hours a day, seven days a week.
Police
File a Police Report
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Join SDPD
Police Website
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San Diego Fire-Rescue Department Home
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About SDFD
The City of San Diego's Fire-Rescue Department encompasses all fire, emergency medical, lifeguard and emergency management services. This includes 9-1-1 services, fire inspections, permits and community education.
Facts and Figures
343 square miles
Population 1,419,845
17 miles of coastline extending 3 miles offshore 4,600 acres around Mission Bay Park
52 fire stations
9 permanent lifeguard stations (31 seasonal stations during peak period)
949 uniformed Fire personnel
98 Permanent uniformed Lifeguard personnel
246 civilian personnel
Incidents CY 2023
Fire
6,006
3.3%
Rescue
1,488
0.8%
Life-Threatening Emergency Response
109,143
59.5%
Urgent Response
27,512
15.0%
Non-Life-Threatening Response
21,766
11.9%
Hazard
14,105
7.7%
Service
1,071
0.6%
Events
266
0.1%
Other
1,993
1.1%
Total
183,350
100%
Mission Statement
To serve the community of San Diego by providing the highest level of emergency/rescue services, hazard prevention and safety education ensuring the protection of life, property and the environment.
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About SDFD
About SDFD Home
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Council Districts
Councilmember Joe LaCava (District 1)
Councilmember Jennifer Campbell (District 2)
Councilmember Stephen Whitburn (District 3)
Council District 4
Councilmember Marni von Wilpert (District 5)
Councilmember Kent Lee (District 6)
Councilmember Raul Campillo (District 7)
Councilmember Vivian Moreno (District 8)
Council President Sean Elo-Rivera (District 9)
City Officials & Independent Offices
Office of Mayor Todd Gloria
City Attorney Mara W. Elliott
Ethics Commission
Office of the City Auditor
Office of the City Clerk
Office of the City Treasurer
Office of the Independent Budget Analyst
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Government Agencies
County of San Diego
State of California
Federal Government
Disclaimers
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談?wù)凞eepSDF中AutoDecoder - 知乎首發(fā)于人工智障領(lǐng)養(yǎng)中心切換模式寫文章登錄/注冊(cè)談?wù)凞eepSDF中AutoDecoderClyce?一個(gè)游離的玻爾茲曼腦文章同步發(fā)布于簡書：日后我的寫作重心可能會(huì)逐步切換到簡書平臺(tái)，因?yàn)樗m合沉下心去讀寫一些東西。前陣子讀了一篇關(guān)于三維重建的論文，是Facebook團(tuán)隊(duì)聯(lián)合華盛頓大學(xué)發(fā)表在CVPR2019上的。這一論文嘗試用神經(jīng)網(wǎng)絡(luò)去近似SDF函數(shù)（將在后面進(jìn)行簡述），進(jìn)而重建三維模型。在閱讀過程中我對(duì)其中的AutoDecoder概念產(chǎn)生了一定興趣，特此將一些隨想記錄下來。在談?wù)揂utoDecoder之前，我想先用最少的文字說說這個(gè)傳說中のDeepSDF。用最簡單易懂的語言來說，對(duì)于一個(gè)已知的三維模型，SDF(Signed Distance Function)就是一個(gè)三維空間中的函數(shù)，輸入一個(gè)坐標(biāo)點(diǎn) p_0 = (x_0, y_0, z_0) ，返回該點(diǎn)到三位模型表面的距離。同時(shí)這個(gè)距離是有符號(hào)的，一般來說，對(duì)于模型外面的點(diǎn)，距離為正，而模型內(nèi)部的點(diǎn)則為負(fù)。或者從觀察者的角度，如果點(diǎn)在表面面向觀察者的一側(cè)，則為正，在背離觀察者的一側(cè)，則為負(fù)。引用DeepSDF論文官方的兔兔說明一下： 各個(gè)方法對(duì)于SDF函數(shù)的存儲(chǔ)往往是離散的，比如將三維空間是做一系列的“體素”（類比二維空間的“像素”），每個(gè)體素中存儲(chǔ)一個(gè)SDF值，之后嘗試用體素中等于或接近零的部分重建一個(gè)三維表面。而DeepSDF論文呢，則嘗試將SDF解析為一個(gè)連續(xù)的函數(shù)。雖然文章把這一點(diǎn)作為一個(gè)創(chuàng)新提出（包括將模型表面視作SDF回歸的決策邊界），其實(shí)我覺得這個(gè)很早應(yīng)該就有人想到了，因?yàn)楦旧蟻碇v，SDF本來就應(yīng)該是一個(gè)連續(xù)函數(shù)才對(duì)，只是受制于各種原因，我們無法為每個(gè)三維物體寫出這個(gè)連續(xù)函數(shù)的完整形式。這也就是DeepSDF的思路，用神經(jīng)網(wǎng)絡(luò)這個(gè)“萬能插值機(jī)”去近似這一函數(shù)。事實(shí)上我之前也想過這個(gè)方向，也相信許多人嘗試過這個(gè)思路。不過就像大多數(shù)DeepLearning落地的應(yīng)用一樣，真正需要做的工作在于如何科學(xué)地編碼輸入輸出，以及如何Encapsulate你自己的領(lǐng)域知識(shí)到神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)和訓(xùn)練過程當(dāng)中去。文章一開始提出了一個(gè)顯而易見的結(jié)構(gòu)，就是直接拿個(gè)神經(jīng)網(wǎng)絡(luò)，輸入坐標(biāo)，輸出SDF值，然后對(duì)每一個(gè)三維模型單獨(dú)訓(xùn)練，充分發(fā)揮“插值機(jī)”的原始作用。不過顯然，這種方法在現(xiàn)實(shí)中是很難應(yīng)用的，因?yàn)閷?duì)于每個(gè)新模型都要訓(xùn)練一個(gè)新神經(jīng)網(wǎng)絡(luò)，實(shí)在是太低效太不~~Generalizablism~~了，那么自然地，為了發(fā)揮深井網(wǎng)絡(luò)Generalizability的優(yōu)勢(shì)，團(tuán)隊(duì)自然而然想到了使用一個(gè)Latent Vector去表示三維模型的原始形態(tài)，于是新的神經(jīng)網(wǎng)絡(luò)就變成了，給定一個(gè)用來表示三維模型的Latent Vector，附帶一個(gè)用以查詢的坐標(biāo)點(diǎn)，返回這個(gè)坐標(biāo)點(diǎn)所在位置的SDF值。具體的神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)和LossFunction的設(shè)計(jì)就不詳述了，非常Straight forward，掃一眼論文就明白。眾所周知（~~并不~~）獲得一個(gè)原始輸入數(shù)據(jù)的Latent Vector Representation，大家最喜歡的方法之一就是訓(xùn)練一個(gè)AutoEncoder。然而DeepSDF則使用了一個(gè)稍有不同的東西，他們叫做AutoDecoder，故名思義，就是一個(gè)不訓(xùn)練Encoder的Decoder。這里我想吐個(gè)大嘈，他們的論文說了一大堆不要Encoder的理由，比如AutoEncoder大家一般都只用一半而把另一半另一半扔掉什么的，說來說去沒說到點(diǎn)上。強(qiáng)烈懷疑他們組提出使用AutoDecoder架構(gòu)的人和最后執(zhí)筆寫論文的人溝通不暢。我自己讀下來的感覺是，AutoDecoder的選用本質(zhì)上是一個(gè)在FeedForward過程的速度，和數(shù)據(jù)本身潛在的復(fù)雜度和變數(shù)之間的取舍（如果我的理解有錯(cuò)，歡迎指正）下面簡單講一下這個(gè)傳說中の AutoDecoder：如圖所示，標(biāo)準(zhǔn)AutoEncoder的架構(gòu)大體上是，一個(gè)輸入，若干層（稱為Encoder）之后有一個(gè)關(guān)鍵的瓶頸層（Code），瓶頸層后面若干層（稱為Decoder）之后有一個(gè)和輸入層長得一樣的輸出層。訓(xùn)練的時(shí)候努力讓輸出等于（甚至優(yōu)于——比如修復(fù)類任務(wù)）輸入，最后訓(xùn)練出來的瓶頸層，就是對(duì)輸入數(shù)據(jù)的一種更緊湊的壓縮，也叫做關(guān)于原始數(shù)據(jù)的Latent Vector。Latent Vector往往包含了原始數(shù)據(jù)最關(guān)鍵的那些信息，更加便于提取特征，等等等等。而DeepSDF則提出，我們可以不要Encoder部分，而直接取用Decoder部分。這就很TM詭異了，如果我只有一個(gè)Decoder，我怎么知道哪個(gè)Latent Vector對(duì)應(yīng)原始輸入呢？官方給出的示意圖長這樣： Hmm...不是很好懂，沒錯(cuò)，不僅這圖不太好懂，論文里的描述也亂七八糟，不過且慢，容我再畫一張圖：這張圖里，我們加入了一個(gè)額外的輸入 X \equiv 1 ，稱為第0層，而 Code 層則是一個(gè)純 Linear 層，那么第 0 層對(duì) Code 層的權(quán)重 W_0 即是我們要的 Latent Vector。另外，Decoder 不必是解碼成原始數(shù)據(jù)的樣子，而是只要解碼成目標(biāo)函數(shù)的輸出（比如SDF值）就好了。那么對(duì)于DeepSDF來說，其結(jié)構(gòu)事實(shí)上可以理解成： 1. 第 0 層是一個(gè)恒為1的輸入， 2. Code 層和要查詢的 (x,y,z) 一起輸入到后面的深度神經(jīng)網(wǎng)絡(luò) 3. 最后輸出SDF函數(shù)而這個(gè)訓(xùn)練過程則是：對(duì)每個(gè)模型 i ，初始化一個(gè)獨(dú)立的Latent Vector v_i （嗯，原論文用 z 表示Latent Vector，不過為了避免和空間坐標(biāo)點(diǎn) (x,y,z) 產(chǎn)生迷惑，我在這里用 v 代替了）然后對(duì)于每個(gè) ((x,y,z),\text{sdf})^i 樣本，選擇該Vector作為到Code層的權(quán)重，即 W^i_{(0\to\text{code})} = z_i^\top ，再對(duì)整個(gè)神經(jīng)網(wǎng)絡(luò)（包含 W^i_{(0\to\text{code})} ）進(jìn)行訓(xùn)練。這樣子訓(xùn)練下來，每個(gè)訓(xùn)練樣本都漸漸就訓(xùn)練出了自己的Latent Vector，而Decoder則學(xué)習(xí)到了對(duì)每個(gè)Latent Vector，如何匹配其要查詢的 (x,y,z) 對(duì)應(yīng)的sdf值?？墒羌热贿@樣，每個(gè)訓(xùn)練數(shù)據(jù)都訓(xùn)練出了自己獨(dú)立的Latent Vector，那么面對(duì)新的數(shù)據(jù)（比如測試數(shù)據(jù)集）該怎么辦？論文里輕描淡寫了一個(gè) \text{argmin}_v ，可是這個(gè) \text{argmin} 是怎么做的呢？難道還是對(duì)每個(gè)新數(shù)據(jù)重新訓(xùn)練一番？！這里不得不吐槽一下，這個(gè)項(xiàng)目不僅論文抓不住重點(diǎn)，代碼也寫得亂七八糟，讀得人暈頭轉(zhuǎn)向，重建的核心部分居然還存在# TODO: why is this needed這樣的注釋。我準(zhǔn)備過一陣子專門針對(duì)機(jī)器學(xué)習(xí)界軟件工程基礎(chǔ)薄弱，代碼凌亂不堪的現(xiàn)象開一篇文章。于是帶著迷惑我又去看了一遍這個(gè)項(xiàng)目的源碼，然后發(fā)現(xiàn)還真的（差不多）是這樣 。。。不過比起直接用 (x,y,z) 作為輸入的那個(gè)版本，這個(gè)版本在面對(duì)新數(shù)據(jù) t 的時(shí)候，鎖定了整個(gè)Decoder，也就是說保持 (v, (x,y,z)) \to \text{sdf value} 的映射不變，而只訓(xùn)練新的 v_t^\top = W^t_{(0\to\text{code})} ，大大減少了需要訓(xùn)練的參數(shù)數(shù)量。并且因?yàn)镈ecoder經(jīng)過之前的訓(xùn)練，已經(jīng)包含了關(guān)于訓(xùn)練數(shù)據(jù)的先驗(yàn)知識(shí)，所以這次Latent Vector訓(xùn)練的收斂將會(huì)十分迅速。其實(shí)這里面我有一個(gè)小小的疑惑，為什么不保留論文最開始的結(jié)構(gòu)（只輸入 (x,y,z) ），但是對(duì)于每個(gè)不同的模型，獨(dú)立訓(xùn)練輸入層對(duì)第一個(gè)隱藏層的權(quán)重，這樣輸入層對(duì)第一個(gè)隱藏層的變換可以視作一個(gè)“Latent Function”（對(duì)應(yīng)“Latent Vector”的概念），將 (x,y,z) 的值變換為該模型對(duì)應(yīng)的，隱空間下該點(diǎn)的特征。這樣一來，神經(jīng)網(wǎng)絡(luò)的參數(shù)數(shù)量能夠減少，并且也能夠更好地表達(dá)Latent Coding和空間坐標(biāo)之間的耦合關(guān)系。這一方面的比較和分析，如果有時(shí)間，我也有可能會(huì)去做一做。所以，答案是，AutoDecoder的結(jié)構(gòu)不能像AutoEncoder一樣面對(duì)新數(shù)據(jù)時(shí)只需要一個(gè)Forward過程，而是需要根據(jù)后面的Decoder中隱含的先驗(yàn)知識(shí)，進(jìn)行少量的訓(xùn)練，以找到一個(gè)Latent Code出來。那么問題來了，為什么要使用一個(gè)無法進(jìn)行實(shí)時(shí)推斷而是每次都要訓(xùn)練的AutoDecoder來代替原有的AutoEncoder呢？一個(gè)不那么具備說服力（但是論文里似乎確實(shí)提出了）的理由是，因?yàn)镈ecoder不必還原原始數(shù)據(jù)，而可以是近似目標(biāo)函數(shù)，所以訓(xùn)練出來的Latent Vector，對(duì)于我們目標(biāo)函數(shù)所需要的信息會(huì)更加友好。但是仔細(xì)想一想就會(huì)發(fā)現(xiàn)，如果我們直接使用原始數(shù)據(jù)進(jìn)行輸入，構(gòu)建一個(gè)神經(jīng)網(wǎng)絡(luò)，然后在某一隱藏層拼接上 (x,y,z) 查詢。那么訓(xùn)練之后，我們就可以得到一個(gè)在預(yù)測時(shí)只需要Forward過程而無需再訓(xùn)練的神經(jīng)網(wǎng)絡(luò)，而被拼接了 (x,y,z) 的那一層也就可以視作Latent Vector。所以……用AutoDecoder的深層原因到底是什么？于是我去費(fèi)勁巴拉地又讀了一遍他們的代碼，著重看了看Reconstruction部分，想辦法搞明白這個(gè)神經(jīng)網(wǎng)絡(luò)到底在做什么之后，謎題便漸漸解開了。首先，這個(gè)模型在重建的時(shí)候到底在干什么？這個(gè)問題在我讀論文和代碼的時(shí)候一度困擾了我很久，尤其是在我看到這個(gè)預(yù)期輸出是SDF Value的神經(jīng)網(wǎng)絡(luò)在 Test - Evaluation 階段，依然在測試數(shù)據(jù)集中包含SDF Value進(jìn)行訓(xùn)練。進(jìn)一步捋順之后我反應(yīng)過來他們?cè)谏窠?jīng)網(wǎng)絡(luò)中的那一步Evaluation，實(shí)際上是在衡量這個(gè)神經(jīng)網(wǎng)絡(luò)能構(gòu)造出一個(gè)多好的Latent Vector來近似測試數(shù)據(jù)集中的SDF Value，而不是嘗試計(jì)算Reconstruct Error。而在真正的表面重建過程中，這個(gè)收斂出來的Latent Vector將和任意輸入坐標(biāo)一起得出一個(gè)SDF Value，進(jìn)而實(shí)現(xiàn)任意精度的重建。那么問題就逐漸明朗了，DeepSDF在做的事情，其實(shí)是對(duì)于有限精度采樣的SDF數(shù)據(jù)，在包含先驗(yàn)知識(shí)的Decoder的幫助下，回歸出一個(gè)連續(xù)的SDF函數(shù)，從而實(shí)現(xiàn)將有窮精度的數(shù)據(jù)轉(zhuǎn)換成任意精度的查詢器，以便進(jìn)行精細(xì)重建。返回來看這個(gè)AutoDecoder在測試數(shù)據(jù)上的訓(xùn)練過程（即尋找Latent Vector的過程），我們發(fā)現(xiàn)，可以將一個(gè)模型的所有采樣點(diǎn)作為一個(gè)完整的epoch，而不是把單個(gè)模型的全部采樣數(shù)據(jù)一股腦輸入進(jìn)去。這么做的好處是什么呢？如果是AutoEncoder，為了得到代表一整個(gè)模型的Latent Vector，我們將不得不想辦法統(tǒng)一模型原始數(shù)據(jù)的分辨率（比如每三個(gè)神經(jīng)元代表一個(gè)采樣點(diǎn)，那么一個(gè)模型最多只能有input_size / 3個(gè)采樣點(diǎn)）。而對(duì)于AutoDecoder，一個(gè)模型對(duì)應(yīng)的采樣數(shù)量只影響其epoch的大小，而不會(huì)影響單個(gè)輸入本身的尺寸，所以AutoDecoder的架構(gòu)支持對(duì)每個(gè)輸入模型有任意數(shù)量的采樣點(diǎn)。另一個(gè)非常重要的是，對(duì)于模型的采樣點(diǎn)，一個(gè)顯著的特征就是它是Orderless的，即你交換任意兩個(gè)采樣點(diǎn)的順序，也不應(yīng)當(dāng)影響最終輸出的結(jié)果。傳統(tǒng)的神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)并不是很善于解碼orderless vector的輸入，但是如果我們不將整個(gè)模型的所有采樣點(diǎn)編碼為一個(gè)巨大的input vector，而是將每個(gè)采樣點(diǎn)作為一個(gè)input vector，整個(gè)模型的所有采樣點(diǎn)作為一個(gè)epoch去訓(xùn)練，那么我們就可以應(yīng)對(duì)這種orderless的特征了。綜上，我們可以得到AutoDecoder結(jié)構(gòu)與AutoEncoder以及論文最開始提出的 (x,y,z) \to \hat{\text{SDF}} 結(jié)構(gòu)（下面簡稱xyz結(jié)構(gòu)）進(jìn)行比較：AutoDecoder vs xyz結(jié)構(gòu)：對(duì)于新的模型，可以比xyz更快速地收斂成一個(gè)可靠的查詢器經(jīng)過訓(xùn)練的Decoder包含了來自訓(xùn)練數(shù)據(jù)集的先驗(yàn)知識(shí)，使得對(duì)于SDF的回歸并不是單純地“插值”操作，而是考慮了模型本身附帶的意義而進(jìn)行的合理補(bǔ)全。AutoDecoder vs AutoEncoderAutoDecoder能夠處理任何尺寸的采樣數(shù)據(jù)，而不必像AutoEncoder那樣將一個(gè)模型的全部采樣點(diǎn)包含在一個(gè)定長的input vector中AutoDecoder將單獨(dú)的Input Vector轉(zhuǎn)換成Epoch的操作，使得采樣的順序能夠被很好地解耦，適合處理orderless的采樣數(shù)據(jù)相比AutoEncoder來說，AutoDecoder面對(duì)每一個(gè)新數(shù)據(jù)時(shí)都需要一個(gè)訓(xùn)練過程來找到合適的Latent Vector，無法在需要相對(duì)實(shí)時(shí)的領(lǐng)域應(yīng)用到這里，AutoDecoder的具體合適的應(yīng)用場景，可謂呼之即出了。不過最后我想說一個(gè)自己的疑惑，不知道大家怎么看待： 將單個(gè)模型的所有采樣數(shù)據(jù)作為一個(gè)Epoch而非一整個(gè)Input Vector去獲取Latent Vector的這一操作，是否因?yàn)樯窠?jīng)網(wǎng)絡(luò)的Independence Assumption而丟失了鄰接節(jié)點(diǎn)的互相影響呢？ 鄙人不才，沒想到足夠的支持或者否定，還希望大家討論點(diǎn)撥編輯于 2020-01-15 16:48計(jì)算機(jī)視覺機(jī)器學(xué)習(xí)?贊同 170??22 條評(píng)論?分享?喜歡?收藏?申請(qǐng)轉(zhuǎn)載?文章被以下專欄收錄人工智障領(lǐng)養(yǎng)中心人工智障的養(yǎng)成
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San Diego police, SDFD lifeguards share details of 911 calls, rescues from January floods – NBC 7 San Diego
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San Diego police, SDFD lifeguards share details of 911 calls, rescues from January floods
The majority of calls were received from the Southcrest and Encanto neighborhoods
By Dana Williams ?
Published February 15, 2024 ?
Updated on February 15, 2024 at 8:52 pm
NBC Universal, Inc.
As the community continues to rebuild after devastating flooding in January that destroyed homes in neighborhoods like Southcrest and Encanto, the San Diego Fire-Rescue Department (SDFD) and the San Diego Police Department (SDPD) are sharing more details about the emergency calls they received on Jan. 22.
According to a report presented to San Diego City Council's Public Safety Committee, there were 985 911 calls processed by the dispatch center that day, a 53% increase over average, and 752 SDFD “first responder incidents” within the city of San Diego, which is a 73% increase over average.
More January storm coverage:
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“We’ve practiced for some of this, right, for when the system gets inundated,” Chief James Gartland, who oversees SDFD lifeguards, told NBC 7.
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Gartland said that day started like any other rainy day. The forecast discussed with the National Weather Service predicted an inch to an inch and a half of rain. The department’s swift water rescue teams were ready and staged at known flood zones, then 911 calls started to pour in.
“We just had, you know, so many calls and there were major, major trouble spots in some of the neighborhoods,” Gartland said. “We were evacuating, you know, 80 people from one street.”
The majority of calls were received from the Southcrest and Encanto neighborhoods. According to Gartland, lifeguards saved approximately 140 people and 30 animals, but it was not easy.
“You have cars floating down, right, you have people trying to cross areas, so all of those hazards are out there,” he said. "That’s why we train as hard as we do.”
Gartland told NBC 7 that lifeguards go to Oregon to train, in part, because they need an environment with rushing water that San Diego doesn’t always have.
“People don’t know, you can’t get the kind of river flow here in Southern California in the time we need to train so lifeguards go away," Gartland said.
Obviously, he said, they could not have prepared for a storm like the one we experienced, but the training they did have helped fill in the gaps. It helped that they recently received new gear from a grant, too.
“We had people out there that were well-trained. They have the right equipment and they executed the mission that we needed them to execute,” he said.
On the other hand, San Diego police officers who were in the field that day were not as prepared, but it didn't stop them from making rescues.
“We don’t carry around things to take people out of houses, evacuate them in water conditions and flooding conditions,” Captain Julie Epperson, with SDPD’s southeastern division, told NBC 7. “My officers were very innovative in that they grabbed paddleboards, they grabbed canoes and they were literally using them just to get people out of the houses on that day because that was the best we had.”
According to Epperson, officers saved approximately 60 people and five animals, all with the limited training and resources they had.
“I think our officers, the ones I’ve talked to and myself as well, we’ve never seen a day like this as far as rain,” she said. “I mean, that will go down in infamy probably as the worst rain that we’ve seen during our tenure here at the police department.”
Epperson has been with the department for close to 30 years. She only recently came to the southeastern division, but said they have been doing what they can to help people recover, including accepting and distributing donations from their division headquarters.
“I will remember it as a tragic day for our community, it was and it still continues to be a tragedy for [residents],” she said. “But, it was a proud moment for us as our officers, and for all first responders, that we did the best we could with having very little time to prepare for it.”
Both Gartland and Epperson agree, there were invaluable lessons learned that will be referenced for generations of San Diego first responders to come.
“I think we now know exactly where the spots are, where the flooding is, the officers know. We’ll learn from that experience,” Epperson said. “Moving forward, our response will improve every time. I hope it never happens again, but if it does, I feel like it was a learning experience for all of us.”
To watch the full presentation presented to the Public Safety Committee by both SDFD and SDPD on their emergency flood response, click here to access the Feb. 14 meeting.
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San Diego’s Public Safety Committee approves SDPD, SDFD use of drone technology – NBC 7 San Diego
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San Diego's Public Safety Committee approves SDPD, SDFD use of drone technology
Next, it will need to be approved by the full San Diego City Council
By Dana Williams ?
Published February 14, 2024 ?
Updated on February 14, 2024 at 8:09 pm
NBC Universal, Inc.
Unmanned aircraft systems, also referred to as UAS systems or drones, have been used by city of San Diego first responders for years.
“In 2018, we had our first mission, and for the next few years, we kind of had a slow rollout while being very careful on the types of missions we would respond to,” Sgt. Nicholas Link with the San Diego Police Department told NBC 7. “It was very much a learning process.”
Link is a certified Federal Aviation Administration drone pilot and the person who developed SDPD’s UAS program. The San Diego Fire-Rescue Department also started their program in 2018. Now, both agencies need to go through the process of being approved under the city’s Transparent and Responsible Use of Surveillance Technology Ordinance (TRUST).
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The ordinance was passed in 2022, then amended earlier this year. It requires that technologies under a broad "surveillance" umbrella used by city employees are revealed to the public and can be evaluated with a privacy advisory board. That includes approximately 300 technologies.
“It’s always been a balance from the beginning,” Link said, in regard to privacy concerns that come with the use of drones. “We have highlighted in our policies and procedures and our training programs a little bit of extra emphasis on understanding what the Fourth Amendment is.”
Link added it is a priority of the department’s to ensure “we always are protecting and respecting people’s privacy while still accomplishing our law enforcement mission.”
As part of the approval process under TRUST, SDPD and SDFD held joint hearings in each of the nine city council districts in early November. It was a requirement but was later amended by the council because of the resources it takes to host them and the lack of attendance from the public. According to Link, only a few people attended.
Then, in mid-November, the agencies presented their programs to the Privacy Advisory Board (PAB). The PAB made several recommendations to both agencies but ultimately suggested that the council approve the use of the drone technology.
Then, the items moved on to the city council's Public Safety Committee. On Wednesday, SDFD and SDPD, including Link, presented to the committee.
“A lot of the recommendations were just simply a desire to change some of the verbiage in the technology use policy,” Link said, referring to the feedback from PAB. “It was actually discovered that the majority of them, the recommendations, were already existing policies and procedures that are documented and we currently use through our department.”
Link said that in the process, so far, no major changes have been made to their drone program. In 2023, they completed approximately 60 missions. He also mentioned they have only been requested twice in six years for special circumstances — both times by city departments. Most recently, one of them was related to the flooding that happened on Jan. 22.
The representative for SDFD who presented also mentioned that many of the flights their drones take are not recorded. They are often used as real-time data for checking hot spots while fighting a fire, for example, and they decided that is not something that needs to be saved. Along the same lines, Link said not all of their flights are recorded, and if they are they are likely considered evidence of a crime, but the public is able to request to see a recording.
The Wednesday meeting ended with unanimous approval of the use of UAS technology pursuant to the TRUST ordinance for both SDFD and SDPD. Next, it will need to be approved by the full city council.
Advocates pushing for more transparency on how those tools are used scored a major win Tuesday.
This article tagged under:
San DiegoSan Diego Police DepartmentSan Diego City CouncilSan Diego Fire-Rescue Department
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